Read_me

1 Raw data

data <- read.csv("/home/bambrozi/2_CORTISOL/Data/T4_Elora_Data_04_25_2024.csv")

# Replace "treated" with NA
data$T4Cortisol[data$T4Cortisol == "treated" | data$T4Cortisol == "Treated at T2" | data$T4Cortisol == "treated at T2"] <- NA
# Convert the column to numeric, coercing non-numeric values to NA
data$T4Cortisol <- as.numeric(as.character(data$T4Cortisol))
#Filtering only the lines with values
data <- data[!is.na(data$T4Cortisol),]
#creating new data file cleaned  
write.csv(data, "/home/bambrozi/2_CORTISOL/Data/data_clean.csv", row.names = F)

print(data)

2 Continuous Phenotype

# Summary Statistics
summary(data$T4Cortisol)
# Histogram
hist(data$T4Cortisol, breaks = 20, main = "Histogram of T4 Cortisol", xlab = "T4 Cortisol")
# Boxplot
boxplot(data$T4Cortisol, main = "Boxplot of T4 Cortisol", ylab = "T4 Cortisol")
# Density Plot
plot(density(data$T4Cortisol), main = "Density Plot of T4 Cortisol", xlab = "T4 Cortisol", ylab = "Density")
# Calculate the theoretical quantiles
qqnorm(data$T4Cortisol, main = "QQ Plot of T4Cortisol", xlim = c(min(qqnorm(data$T4Cortisol)$x), max(qqnorm(data$T4Cortisol)$x)), ylim = c(min(qqnorm(data$T4Cortisol)$y), max(qqnorm(data$T4Cortisol)$y) + 2 * IQR(qqnorm(data$T4Cortisol)$y)))
# Add the QQ line
qqline(data$T4Cortisol, col = "red")

Summary statistics

Histogram

Density

Box_Plot

qq_Plot

Shapiro-Wilk normality test

2.1 Categorical Phenotype

I received from Umesh a e-mail informing the three categories that the animals could be sorted based on their cortisol concentration.

data$Categorical <- ifelse(data$T4Cortisol >= 956, "H", 
                           ifelse(data$T4Cortisol <= 190.8, "L", "M"))

table(data$Categorical)
library(ggplot2)

# Reorder the levels of the 'Categorical' column
data$Categorical <- factor(data$Categorical, levels = c("L", "M", "H"))

# Create the histogram with reordered categories
ggplot(data, aes(x = Categorical, fill = Categorical)) +
  geom_bar() +
  labs(title = "Histogram of T4Cortisol by Category",
       x = "Category",
       y = "Frequency") +
  theme_minimal()

# Create the histogram
ggplot(data, aes(x = T4Cortisol, fill = Categorical)) +
  geom_histogram(binwidth = 50, color = "black", alpha = 0.7) + # Adjust binwidth as needed
  labs(title = "Histogram of T4Cortisol with Color by Category",
       x = "T4 Cortisol",
       y = "Frequency",
       fill = "Category") +
  scale_fill_manual(values = c("H" = "red", "M" = "blue", "L" = "green")) + # Adjust colors if needed
  theme_minimal()

# Create the density plot
ggplot(data, aes(x = T4Cortisol, fill = Categorical)) +
  geom_density(alpha = 0.3) +
  labs(title = "Density Plot of T4Cortisol with Color by Category",
       x = "T4Cortisol",
       y = "Density",
       fill = "Category") +
  scale_fill_manual(values = c("H" = "red", "M" = "blue", "L" = "green")) + # Adjust colors if needed
  theme_minimal()

# Create a density plot
ggplot(data, aes(x = T4Cortisol)) +
  geom_density() +
  geom_vline(xintercept = c(956, 190.8), linetype = "dashed", color = "red") +
  labs(title = "Density Plot of T4Cortisol with Vertical Lines",
       x = "T4Cortisol",
       y = "Density") +
  theme_minimal()

The animals were sorted in these three categories >H = Hight >M = Medium >L = Low

The individuals frequency distribution in theese categories are shown in the plots below

2.2 Removing “outliers”

Observing the previous plots I tried to remove the “outliers” phenotypes above 1250, but the outcome from Shapiro test is still indicating no normality of the data.

library(tidyverse)

data_no_out <- filter(data, T4Cortisol < 1250)

# Create QQ plot
qqnorm(data_no_out$T4Cortisol, main = "QQ Plot of T4Cortisol", xlab = "Theoretical Quantiles", ylab = "Sample Quantiles")
qqline(data$T4Cortisol, col = "red")

boxplot(data_no_out$T4Cortisol, main = "Boxplot of T4 Cortisol", ylab = "T4 Cortisol")

hist(data_no_out$T4Cortisol, breaks = 20, main = "Histogram of T4 Cortisol", xlab = "T4 Cortisol")

shapiro.test(data$T4Cortisol)

3 GENOTYPES

Lucas Alcântara sent me the path to the genotype and pedigree files: /data/cgil/daiclu/6_Data/database/public_output/bruno

In this folder we found the following files:

• bruno_gntps.txt: the file with the genotypes in 012 format
• bruno_ids.csv: file with elora_id, cdn_id, iid
• bruno_ped.id.csv: pedigree with cdn_id
• bruno_ped.iid.csv: pedigree with iid

I made a copy of this files in a folder called Raw_files:

/home/bambrozi/2_CORTISOL/RawFiles

This directory has two sub-directories:

• Genotypes
• Pedigree

3.1 Transforming the Genotypes in 012 format to plink format

To perfome this I used two codes

1. Add “column names”

sed -i '1i id Call' genotypes.txt

2. Recode in python

filename = 'bruno_gntps.txt'

outputFileOpen = open('genoplink.ped','w')


recode = {'0':['C','C'] , '1':['A','C'] , '2':['A','A'] , '5':['0','0'] }
for line in open(filename,'r'):
    if 'Call' in line : continue

    ids, call = line.strip().split()
    genotypes = [ recode[geno012][0] +' '+ recode[geno012][1] for geno012 in call ]

    outputFileOpen.write("%s %s %s %s %s %s %s\n" % ('HO', ids, '0','0','0','-9',' '.join(genotypes)) )

Now I have a folder called /home/bambrozi/2_CORTISOL/Geno_files with the file named genoplink.ped

4 GWAS

4.1 Checking with Phenotyped Animals also have Genotype

library(data.table)

pheno <- read.csv("/home/bambrozi/2_CORTISOL/Data/data_clean.csv")
ped <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/Pedigree/bruno_ids.csv")
geno <- fread("/home/bambrozi/2_CORTISOL/Geno_files/genoplink.ped")
geno <- geno[,c("V2")]

#Bringing cdn_id to my phenotype file
#Generate a index with the match
matching_indices <- match(pheno$ID, ped$elora_id)
# Then, assign 'cdn_id' from 'ped' to 'pheno' where there are matches
pheno$cdn_id <- ifelse(!is.na(matching_indices), ped$cdn_id[matching_indices], NA)

#Making a phenotype file only with genotyped animals
pheno_genotyped <- pheno[pheno$cdn_id %in% geno$V2,] 

#check if all animals in this file are genotyped
checkk <- pheno_genotyped$cdn_id %in% geno$V2
sum(checkk)

4.2 Generating a Phenotype file

The phenotype file should have three columns: FID, Animal_id, Phenotype

HO <- rep("HO", 252)

pheno_gwas <- as.data.frame(cbind(HO, pheno_genotyped$cdn_id, pheno_genotyped$T4Cortisol))

colnames(pheno_gwas) <- c("FID", "cdn_id", "cortisol")

pheno_gwas$cdn_id <-  as.numeric(pheno_gwas$cdn_id)
pheno_gwas$cortisol <- round(as.numeric(pheno_gwas$cortisol),2)

write.table(pheno_gwas, "/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt", quote = F, row.names = F, col.names = T)

4.3 Adjusting the SNP_map to .map

map <- fread("/data/cgil/daiclu/6_Data/database/public_output/bruno/DGVsnpinfo.2404.ho")
morgan <- data.frame(X0 = rep(0, 45101))
mapa=as.data.frame(cbind(map$chr, map$snp_name, morgan$X0, map$location))
head(mapa)
write.table(x = mapa, file = "/home/bambrozi/2_CORTISOL/Geno_files/genoplink.map", row.names = FALSE, col.names = FALSE, sep = "\t", quote = FALSE)

4.4 Generating the bfiles

system("/home/local/bin/plink --cow --nonfounders --allow-no-sex --keep-allele-order --file /home/bambrozi/2_CORTISOL/Geno_files/genoplink --make-bed --out /home/bambrozi/2_CORTISOL/Geno_files/genoplink")

With the code above I generated the bfiles:

genoplink.bed
• genoplink.bim
• genoplink.fam
• genoplink.log
• genoplink.nosex

5 Quality Control

We ran the code below to perfom the QC

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files/genoplink
RESULT=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean

/home/local/bin/plink \
    --bfile ${DATA} \
    --cow \
    --allow-no-sex \
    --hwe 1e-5 \
    --maf 0.01 \
    --geno 0.1 \
    --mind 0.1 \
    --keep-allele-order \
    --make-bed \
    --out ${RESULT}
    

The server screen outcome is shown below.

After the Quality Control we ended up with

• 252 samples
• 42,278 variants (SNPs)

6 KING

To check for duplicated individuals I performed the KINSHIP analysis using one script from Larissa Braga. Running the King Analysis on Plink.

#!/bin/bash

DATA=/home/bambrozi/Extrm_ARS1_GrassHill_1/GENOTYPES/ONLY_GRASSHILL_AND_PHENO_after_QC/only_grasshill_and_pheno_clean
RESULT=/home/bambrozi/Extrm_ARS1_GrassHill_1/GENOTYPES/KING/result_king

plink2 \
    --bfile ${DATA} \
    --chr-set 29 \
    --make-king-table \
    --out ${RESULT}

This is the output screen on terminal:

The table below is the output /home/bambrozi/2_CORTISOL/Geno_files_after_KING/result_king.kin0 and have pairwise comparisons between all individuals.

Now we should open in R and check for individuals with more than 0,354, to perform this we can use the code below, also provided by Larissa Braga:

setwd("/home/bambrozi/2_CORTISOL/Geno_files_after_KING")

relatedness="result_king.kin0" ## change accordingly!!

library(data.table)

print(relatedness)
rel=fread(relatedness, h = T)
head(rel)

print("Individuals with different identifications above the cut off of 0.354:")
dup=subset(rel, KINSHIP >= 0.354  & IID1!=IID2)
print(dup)
nrow(dup)

So the code above will provide this output if there is any duplicated individual.

We do not have any duplicated individual

So the file to be used are those in the directory /home/bambrozi/2_CORTISOL/Geno_files_after_QC

files:genoplink_clean

After Quality Control we didn’t lost any animal, so we don’t need to update our phenotype file

7 PCA

Now before performing the PCA analysis we need to change the FID for those individuals that has phenotype = 1 for Nadia.

#!/bin/bash

DATA=/home/bambrozi/Extrm_ARS1_GrassHill_1/PCA/imput_pca
RESULT=/home/bambrozi/Extrm_ARS1_GrassHill_1/PCA/pca_result

plink \
    --bfile ${DATA} \
    --keep-allele-order \
    --chr-set 29 \
    --pca \
    --out ${RESULT}

The PCA output:

7.1 PCA Plot

After generate the Eigenvalues and Eigenvectors I used the code below to generate the PCA Plot

setwd("/home/bambrozi/2_CORTISOL/PCA")

library(ggplot2) 
library(tidyverse)

##
# Visualize PCA results
###

# read in result files
eigenValues <- read_delim("pca_result.eigenval", delim = " ", col_names = F)
eigenVectors <- read_delim("pca_result.eigenvec", delim = " ", col_names = F)

## Proportion of variation captured by each vector
eigen_percent <- round((eigenValues / (sum(eigenValues))*100), 2)


# PCA plot
png("pca-plink.eng.png", width=5, height=5, units="in", res=300)
ggplot(data = eigenVectors) +
  geom_point(mapping = aes(x = X3, y = X4), color = "red", shape = 19, size = 1, alpha = 1) +
  geom_hline(yintercept = 0, linetype="dotted") +
  geom_vline(xintercept = 0, linetype="dotted") +
  labs(x = paste0("Principal component 1 (", eigen_percent[1,1], " %)"),
       y = paste0("Principal component 2 (", eigen_percent[2,1], " %)")) +
  theme_minimal()
dev.off()


# PCA plot with animal ids
png("pca-plink.eng.animals_id.png", width=50, height=50, units="in", res=300)
ggplot(data = eigenVectors) +
  geom_point(mapping = aes(x = X3, y = X4), color = "red", shape = 19, size = 5, alpha = 1) +
  geom_text(mapping = aes(x = X3, y = X4, label = X2), size = 2, hjust = 0, vjust = 0) +  # Add labels for animal IDs
  geom_hline(yintercept = 0, linetype="dotted") +
  geom_vline(xintercept = 0, linetype="dotted") +
  labs(x = paste0("Principal component 1 (", eigen_percent[1,1], " %)"),
       y = paste0("Principal component 2 (", eigen_percent[2,1], " %)")) +
  theme_minimal()
dev.off()

8 GWAS on GCTA

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean
RESULT=/home/bambrozi/2_CORTISOL/GWAS/GWAS_result
PHENO=/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt

/home/local/bin/gcta \
    --bfile ${DATA} \
    --mlma-loco \
    --pheno ${PHENO} \
    --autosome-num 29 \
    --autosome \
    --out ${RESULT}

This is te output from the code above:

9 5%

9.1 Manhattan Plot - Bonferroni

gwas<- read.table(file = "/home/bambrozi/2_CORTISOL/GWAS/GWAS_result.loco.mlma", 
                  head=T, stringsAsFactors = F)
gwas$Chr<- as.factor(gwas$Chr)
gwas$logP<- -log10(gwas$p)
rmv<- which(gwas$logP == "NaN")
if (length(rmv) >=1) {gwas <- gwas[-rmv,]}
bonf<- -log10(0.05/nrow(gwas))

library(GHap)
ghap.manhattan(data=gwas,chr="Chr", bp="bp", y="logP", type="p", pch = 20, 
               cex=1, lwd=1, ylab="", xlab="Chromossomes", 
               main="GWAS Cortisol", backcolor="#F5EFE780", chr.ang=0,)
abline(h=(bonf), col="red2")
legend("topleft", col="red2", lwd=2, c("Bonferroni correction"), bty="n")

The code above creates a Manhattam Plot, the correction for multiple test is the Bonferroni correction

The code below will save information of Significant SNP.

library(tidyverse)

SNP_sig_bonf <- filter(gwas, logP > bonf)
write.table(SNP_sig_bonf, file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

Below we can see the 1 significant SNP for Bonferroni correction.

9.2 FDR

gwas$bh <- p.adjust(gwas$p, method = "BH") #FDR 

The code below will create a file with the significant snp for FDR-BH

SNP_sig_BH <- filter(gwas, bh < 0.05)
write.table(SNP_sig_BH, file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_BH.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

As outcome we can see that the FDR-BH method didn’t increase the number of significant SNP.

9.3 Corrected Bonferroni for genome independent segments

Now we are going to apply a correction for multiple testing modifying the Bonferroni test adjusting not for the total number of SNPs but for the number of the independent segments in the genome.

Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)

# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8

# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)

NeL <- Ne*L_M

# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)


gwas<- read.table(file = "/home/bambrozi/2_CORTISOL/GWAS/GWAS_result.loco.mlma", 
                  head=T, stringsAsFactors = F)

gwas$Chr<- as.factor(gwas$Chr)
gwas$logP<- -log10(gwas$p)
rmv<- which(gwas$logP == "NaN")
if (length(rmv) >=1) {gwas <- gwas[-rmv,]}

bonf<- -log10(0.05/Me)

library(GHap)
ghap.manhattan(data=gwas,chr="Chr", bp="bp", y="logP", type="p", pch = 20, 
               cex=1, lwd=1, ylab="", xlab="Chromossomes", 
               main="GWAS Cortisol", backcolor="#F5EFE780", chr.ang=0,)
abline(h=(bonf), col="red2")
legend("topleft", col="red2", lwd=2, c("Bonferroni corr. for ind. segments"), bty="n")


library(tidyverse)

SNP_sig_bonf_ind_seg <- filter(gwas, logP > bonf)
write.table(SNP_sig_bonf_ind_seg, file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

Below we can find the list of significant SNPs after the correction for Multiple Testing

10 qqPlot

I performed the qqPlot analysis using the code below:

gwas<- read.table("GWAS_result.loco.mlma", h=T)
ps<- gwas$p
inflation <- function(ps) {
  chisq <- qchisq(1 - ps, 1)
  lambda <- median(chisq) / qchisq(0.5, 1)
  lambda
}
# Calculating the lambda -  the lambda statistic should be close to 1 if
#the points fall within the expected range, or greater than one if 
# the observed p-values are more significant than expected.
inflation(ps)

bonf<- -log10(0.05/nrow(gwas))

gwas$log<- -log10(gwas$p)

gg_qqplot <- function(ps, ci = 0.95) {
  n  <- length(ps)
  df <- data.frame(
    observed = -log10(sort(ps)),
    expected = -log10(ppoints(n)),
    clower   = -log10(qbeta(p = (1 - ci) / 2, shape1 = 1:n, shape2 = n:1)),
    cupper   = -log10(qbeta(p = (1 + ci) / 2, shape1 = 1:n, shape2 = n:1))
  )
  log10Pe <- expression(paste("Expected -log"[10], plain(P)))
  log10Po <- expression(paste("Observed -log"[10], plain(P)))
  ggplot(df) +
    geom_ribbon(
      mapping = aes(x = expected, ymin = clower, ymax = cupper),
      alpha = 0.1
    ) +
    geom_point(aes(expected, observed), shape = 1, size = 3) +
    geom_abline(intercept = 0, slope = 1, alpha = 0.5) +
    # geom_line(aes(expected, cupper), linetype = 2, size = 0.5) +
    # geom_line(aes(expected, clower), linetype = 2, size = 0.5) +
    xlab(log10Pe) +
    ylab(log10Po)
}

## plot -----
gg_qqplot(ps) +
  theme_bw(base_size = 24) +
  annotate(
    geom = "text",
    x = -Inf,
    y = Inf,
    hjust = -0.15,
    vjust = 1 + 0.15 * 3,
    label = sprintf("λ = %.2f", inflation(ps)),
    size = 8
  ) +
  theme(
    axis.ticks = element_line(size = 0.5),
    panel.grid = element_blank()
    # panel.grid = element_line(size = 0.5, color = "grey80")
  )

The outcome is the qqplot below, and the lambda value is \(\lambda\) = 1.03

11 VEP - Variant Effect Prediction

After insert all rsID on VEP the summary is shown below:

Bellow I’m gonna show the “genome view” for each SNP (variant) recovered from VEP:

rs42217767 ps. this variant wasn’t recovered automaticaly by VEP, so I typed the chr and position on the search area of the “genome viewer”

rs110031217

rs110991998

rs42089058

rs41644634

rs41567074

ps. the unique reference genome available in the VEP is the ARS-UCD1.3, which is not a problem once it is working with the rsID, which for sure is our variant.

12 GALLO

# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#import MARKER files = the GWAS output
gwas <- read.table(file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg.txt", 
                   head=T, stringsAsFactors = F)

# Assuming "gwas" is your dataframe
gwas <- subset(gwas, select = c(Chr, SNP, bp))


colnames(gwas) <- c("CHR","SNP", "BP")


#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.table(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/out_genes_50k.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/out_genes_50k.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/out_qtl_50k.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("CHR", "SNP", "BP", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/out_qtl_50k_clean.csv")

Dowloading the .gtf file from Ensembl https://useast.ensembl.org/info/data/ftp/index.html

Downloading the .gff file from AnimalQTLdb https://www.animalgenome.org/cgi-bin/QTLdb/index

The GALLO output are bellow:

For GENES

X	CHR	SNP	BP	chr	start_pos	end_pos	width	strand	gene_id	gene_name	gene_biotype
1	11	BTB-01060598	32301594	11	32278324	32766620	488297	-	ENSBTAG00000046199	NRXN1	protein_coding
2	11	BTB-01060598	32301594	11	32280416	32280512	97	-	ENSBTAG00000044573	U6	snRNA
3	22	ARS-BFGL-NGS-26419	46052082	22	45924535	46818511	893977	-	ENSBTAG00000013117	CACNA2D3	protein_coding
4	22	ARS-BFGL-NGS-26419	46052082	22	46051998	46062657	10660	+	ENSBTAG00000013124	LRTM1	protein_coding
5	22	ARS-BFGL-NGS-23411	5134078	22	5091037	5184321	93285	+	ENSBTAG00000019832	TGFBR2	protein_coding
6	26	BTB-00926636	17857480	26	17703169	17846407	143239	-	ENSBTAG00000011743	TLL2	protein_coding
7	26	BTB-00926636	17857480	26	17851539	17912665	61127	-	ENSBTAG00000016298	TM9SF3	protein_coding
8	26	BTA-62125-no-rs	17899619	26	17851539	17912665	61127	-	ENSBTAG00000016298	TM9SF3	protein_coding
9	26	BTA-62125-no-rs	17899619	26	17925643	18029878	104236	-	ENSBTAG00000019872	PIK3AP1	protein_coding
10	28	BTA-99379-no-rs	41666186	28	41609015	41649016	40002	-	ENSBTAG00000007540	GLUD1	protein_coding
11	28	BTA-99379-no-rs	41666186	28	41650333	41763371	113039	+	ENSBTAG00000019194	SHLD2	protein_coding

FOR QTLs

X	CHR	SNP	BP	QTL_type	start_pos	end_pos	QTL_ID
1	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5109498	5109502	151609
2	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5134076	5134080	151620
3	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5173197	5173201	151601
4	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51802
5	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51803
6	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51804
7	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51805
8	22	ARS-BFGL-NGS-26419	46052082	Production	46052080	46052084	51806
9	22	ARS-BFGL-NGS-26419	46052082	Production	46052080	46052084	51807
10	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51808
11	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51809
12	22	ARS-BFGL-NGS-26419	46052082	Health	46052080	46052084	51810
13	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51811
14	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51812
15	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51813
16	22	ARS-BFGL-NGS-26419	46052082	Milk	46073517	46073521	243365
17	26	BTB-00926636	17857480	Milk	17809768	17809772	195264
18	26	BTB-00926636	17857480	Milk	17809768	17809772	200269
19	26	BTB-00926636	17857480	Milk	17809768	17809772	200270
20	26	BTB-00926636	17857480	Milk	17809768	17809772	205295
21	26	BTB-00926636	17857480	Milk	17810363	17810367	198859
22	26	BTB-00926636	17857480	Milk	17810363	17810367	203868
23	26	BTB-00926636	17857480	Milk	17810363	17810367	203869
24	26	BTB-00926636	17857480	Milk	17810363	17810367	210297
25	26	BTB-00926636	17857480	Milk	17811021	17811025	198858
26	26	BTB-00926636	17857480	Milk	17811021	17811025	203866
27	26	BTB-00926636	17857480	Milk	17811021	17811025	203867
28	26	BTB-00926636	17857480	Milk	17811021	17811025	210296
29	26	BTB-00926636	17857480	Milk	17811725	17811729	197493
30	26	BTB-00926636	17857480	Milk	17811725	17811729	202704
31	26	BTB-00926636	17857480	Milk	17812273	17812277	195685
32	26	BTB-00926636	17857480	Milk	17812273	17812277	200785
33	26	BTB-00926636	17857480	Milk	17813212	17813216	195285
34	26	BTB-00926636	17857480	Milk	17813212	17813216	200301
35	26	BTB-00926636	17857480	Milk	17813978	17813982	196236
36	26	BTB-00926636	17857480	Milk	17813978	17813982	201468
37	26	BTB-00926636	17857480	Milk	17815237	17815241	198002
38	26	BTB-00926636	17857480	Milk	17815237	17815241	203190
39	26	BTB-00926636	17857480	Milk	17817223	17817227	34750
40	26	BTB-00926636	17857480	Milk	17817223	17817227	195586
41	26	BTB-00926636	17857480	Milk	17821297	17821301	197235
42	26	BTB-00926636	17857480	Milk	17821297	17821301	202455
43	26	BTB-00926636	17857480	Milk	17822209	17822213	197783
44	26	BTB-00926636	17857480	Milk	17822209	17822213	202983
45	26	BTB-00926636	17857480	Milk	17823105	17823109	196204
46	26	BTB-00926636	17857480	Milk	17823105	17823109	201419
47	26	BTB-00926636	17857480	Milk	17824770	17824774	196867
48	26	BTB-00926636	17857480	Milk	17824770	17824774	202101
49	26	BTB-00926636	17857480	Milk	17833551	17833555	195748
50	26	BTB-00926636	17857480	Milk	17833551	17833555	200855
51	26	BTB-00926636	17857480	Milk	17833551	17833555	206213
52	26	BTB-00926636	17857480	Milk	17865069	17865073	197144
53	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	197144
54	26	BTB-00926636	17857480	Milk	17865069	17865073	202367
55	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	202367
56	26	BTB-00926636	17857480	Milk	17865069	17865073	208485
57	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	208485
58	26	BTB-00926636	17857480	Milk	17871201	17871205	197611
59	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	197611
60	26	BTB-00926636	17857480	Milk	17871201	17871205	202816
61	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	202816
62	26	BTB-00926636	17857480	Milk	17871201	17871205	209076
63	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	209076
64	26	BTB-00926636	17857480	Milk	17885871	17885875	63656
65	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	63656
66	26	BTB-00926636	17857480	Milk	17885871	17885875	199068
67	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	199068
68	26	BTB-00926636	17857480	Milk	17885871	17885875	204113
69	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	204113
70	26	BTB-00926636	17857480	Milk	17885871	17885875	204114
71	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	204114
72	26	BTB-00926636	17857480	Milk	17885871	17885875	210399
73	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	210399
74	26	BTB-00926636	17857480	Milk	17895273	17895277	63657
75	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	63657
76	26	BTB-00926636	17857480	Milk	17895273	17895277	199069
77	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	199069
78	26	BTB-00926636	17857480	Milk	17895273	17895277	204115
79	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	204115
80	26	BTB-00926636	17857480	Milk	17895273	17895277	204116
81	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	204116
82	26	BTB-00926636	17857480	Milk	17895273	17895277	210400
83	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	210400
84	26	BTB-00926636	17857480	Milk	17895803	17895807	199070
85	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	199070
86	26	BTB-00926636	17857480	Milk	17895803	17895807	204117
87	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	204117
88	26	BTB-00926636	17857480	Milk	17895803	17895807	204118
89	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	204118
90	26	BTB-00926636	17857480	Milk	17895803	17895807	210401
91	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	210401
92	26	BTB-00926636	17857480	Milk	17900304	17900308	199071
93	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	199071
94	26	BTB-00926636	17857480	Milk	17900304	17900308	204119
95	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	204119
96	26	BTB-00926636	17857480	Milk	17900304	17900308	204120
97	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	204120
98	26	BTB-00926636	17857480	Milk	17900304	17900308	210402
99	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	210402
100	26	BTB-00926636	17857480	Milk	17902932	17902936	63658
101	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	63658
102	26	BTB-00926636	17857480	Milk	17902932	17902936	199072
103	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	199072
104	26	BTB-00926636	17857480	Milk	17902932	17902936	204121
105	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	204121
106	26	BTB-00926636	17857480	Milk	17902932	17902936	204122
107	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	204122
108	26	BTB-00926636	17857480	Milk	17902932	17902936	210403
109	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	210403
110	26	BTB-00926636	17857480	Milk	17906861	17906865	63660
111	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	63660
112	26	BTB-00926636	17857480	Milk	17906861	17906865	199074
113	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	199074
114	26	BTB-00926636	17857480	Milk	17906861	17906865	204125
115	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	204125
116	26	BTB-00926636	17857480	Milk	17906861	17906865	204126
117	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	204126
118	26	BTB-00926636	17857480	Milk	17906861	17906865	210405
119	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	210405
120	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	63654
121	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	199076
122	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	204129
123	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	204130
124	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	210407
125	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	63655
126	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	197826
127	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	203026
128	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	203027
129	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	209354
130	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	195860
131	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	201008
132	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	201009
133	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	206462
134	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	33984
135	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195668
136	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195669
137	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195670
138	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	200758
139	26	BTA-62125-no-rs	17899619	Milk	17931209	17931213	199092
140	26	BTA-62125-no-rs	17899619	Milk	17931209	17931213	204151
141	28	BTA-99379-no-rs	41666186	Reproduction	41646434	41646438	107048
142	28	BTA-99379-no-rs	41666186	Reproduction	41646434	41646438	107227

12.1 QTL annotation on GALLO

QTL type

#GALLO
par(mar=c(8,20,8,8))
plot_qtl_info(out.qtl, qtl_plot = "qtl_type", cex=1)

QTL type

#GALLO
par(mar=c(10,20,10,10))
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Reproduction")

par(mar=c(10,20,10,10))
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Health")

Reproduction

Health

12.2 QTL enrichment on GALLO

#GALLO
#QTL enrichment analysis 
out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]

write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/out_enrich_qtl_genome_name.csv")

out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/out_enrich_qtl_genome_type.csv")

#Plots

#Name

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL," - ","CHR",out.enrich_qtl_name$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL," - ","CHR",out.enrich_qtl_type$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval	QTL_type
5	Milk C14 index	35	4437	108	163224	0.0000000	0.0000000	Milk
9	Milk myristoleic acid content	26	3047	108	163224	0.0000000	0.0000000	Milk
10	Milk palmitoleic acid content	15	2422	108	163224	0.0000000	0.0000000	Milk
6	Milk C16 index	12	2002	108	163224	0.0000000	0.0000001	Milk
12	Muscle carnosine content	3	67	108	163224	0.0000131	0.0000497	Meat and Carcass
14	Pregnancy rate	2	944	108	163224	0.1296627	0.4105985	Reproduction
18	Teat length	1	300	108	163224	0.1802436	0.4892327	Exterior
15	Rear leg placement - side view	1	430	108	163224	0.2479752	0.5235032	Exterior
17	Stillbirth	2	1363	108	163224	0.2280294	0.5235032	Reproduction
3	Foot angle	1	672	108	163224	0.3596272	0.6162877	Exterior
7	Milk capric acid content	1	912	108	163224	0.4541067	0.6162877	Milk
8	Milk myristic acid content	1	902	108	163224	0.4504612	0.6162877	Milk
13	Net merit	1	903	108	163224	0.4508269	0.6162877	Production
19	Udder depth	1	695	108	163224	0.3693423	0.6162877	Exterior
16	Somatic cell score	1	1122	108	163224	0.5253603	0.6654564	Health
2	Conception rate	1	1255	108	163224	0.5656375	0.6716945	Reproduction
1	Calving ease	2	3819	108	163224	0.7219243	0.7776744	Reproduction
4	Length of productive life	1	2004	108	163224	0.7367441	0.7776744	Production
11	Milk yield	1	6432	108	163224	0.9870080	0.9870080	Milk

Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval
1	Exterior	4	9077	108	163224	0.8569775	0.9999953
2	Health	1	5889	108	163224	0.9811250	0.9999953
3	Meat and Carcass	3	18258	108	163224	0.9997109	0.9999953
4	Milk	91	75352	108	163224	0.0000000	0.0000000
5	Production	2	19640	108	163224	0.9999848	0.9999953
6	Reproduction	7	35008	108	163224	0.9999953	0.9999953

13 GPROFILER

online version: https://biit.cs.ut.ee/gprofiler/gost

I got a script from Julia Rodrigues about the R package GPROFILER to perform an enrichment of my Genes.

### enriquecimento genico
#install.packages("gprofiler2")
library(gprofiler2)

#Para conferir a lista de organism -> https://biit.cs.ut.ee/gprofiler/page/organism-list

#Obs: eu entro com os ids ENSOAR...
query <- read.table ("/home/bambrozi/2_CORTISOL/GALLO/out_genes_50k.txt", header = T)
query <- query[,c("gene_id")]

gene_enrich <- gost(
  query,
  organism = "btaurus", 
  ordered_query = FALSE,
  multi_query = FALSE,
  significant = TRUE,
  exclude_iea = FALSE,
  measure_underrepresentation = FALSE,
  evcodes = TRUE,
  user_threshold = 0.05,
  correction_method = c("fdr"),
  domain_scope = c("annotated"),
  numeric_ns = "",
  sources = NULL,
  as_short_link = FALSE,
  highlight = FALSE
)


#str(gene_enrich) # para ver o formato dos meus dados

#selecionando apenas as informações da lista que me interessam para fazer meu data.frame 
result_enrich <- data.frame(gene_enrich$result)
result_enrich <- data.frame(Category = result_enrich$source,
                            ID = result_enrich$term_id,
                            Term = result_enrich$term_name,
                            adj_pvalue = result_enrich$p_value,
                            id_ensembl = result_enrich$intersection)

write.table(result_enrich,"/home/bambrozi/2_CORTISOL/GPROFILER/gene_enrich.txt", col.names=TRUE, row.names=FALSE, sep="\t", quote=F)
write.csv(result_enrich,"/home/bambrozi/2_CORTISOL/GPROFILER/gene_enrich.csv")

Below we can se the significant terms of this enrichment (output):

X	Category	ID	Term	adj_pvalue	id_ensembl
1	GO:BP	GO:0006538	glutamate catabolic process	0.0383792	ENSBTAG00000007540
2	GO:BP	GO:0003274	endocardial cushion fusion	0.0383792	ENSBTAG00000019832
3	GO:BP	GO:1905005	regulation of epithelial to mesenchymal transition involved in endocardial cushion formation	0.0383792	ENSBTAG00000019832
4	GO:BP	GO:1905007	positive regulation of epithelial to mesenchymal transition involved in endocardial cushion formation	0.0383792	ENSBTAG00000019832
5	GO:BP	GO:0003430	growth plate cartilage chondrocyte growth	0.0383792	ENSBTAG00000019832
6	GO:BP	GO:0003431	growth plate cartilage chondrocyte development	0.0383792	ENSBTAG00000019832
7	GO:BP	GO:0003433	chondrocyte development involved in endochondral bone morphogenesis	0.0383792	ENSBTAG00000019832
8	GO:BP	GO:0003415	chondrocyte hypertrophy	0.0383792	ENSBTAG00000019832
9	GO:BP	GO:0048631	regulation of skeletal muscle tissue growth	0.0383792	ENSBTAG00000011743
10	GO:BP	GO:0051138	positive regulation of NK T cell differentiation	0.0383792	ENSBTAG00000019832
11	GO:BP	GO:0062043	positive regulation of cardiac epithelial to mesenchymal transition	0.0383792	ENSBTAG00000019832
12	GO:BP	GO:0051136	regulation of NK T cell differentiation	0.0383792	ENSBTAG00000019832
13	GO:BP	GO:0003186	tricuspid valve morphogenesis	0.0383792	ENSBTAG00000019832
14	GO:BP	GO:0062042	regulation of cardiac epithelial to mesenchymal transition	0.0383792	ENSBTAG00000019832
15	GO:BP	GO:0060434	bronchus morphogenesis	0.0383792	ENSBTAG00000019832
16	GO:BP	GO:0060440	trachea formation	0.0383792	ENSBTAG00000019832
17	GO:BP	GO:0060462	lung lobe development	0.0383792	ENSBTAG00000019832
18	GO:BP	GO:0060463	lung lobe morphogenesis	0.0383792	ENSBTAG00000019832
19	GO:BP	GO:0048632	negative regulation of skeletal muscle tissue growth	0.0383792	ENSBTAG00000011743
20	GO:BP	GO:0003175	tricuspid valve development	0.0383792	ENSBTAG00000019832
21	GO:BP	GO:0061520	Langerhans cell differentiation	0.0383792	ENSBTAG00000019832
22	GO:BP	GO:1905317	inferior endocardial cushion morphogenesis	0.0383792	ENSBTAG00000019832
23	GO:BP	GO:2000563	positive regulation of CD4-positive, alpha-beta T cell proliferation	0.0383792	ENSBTAG00000019832
24	GO:BP	GO:1990428	miRNA transport	0.0383792	ENSBTAG00000019832
25	GO:BP	GO:0002513	tolerance induction to self antigen	0.0383792	ENSBTAG00000019832
26	GO:BP	GO:0002514	B cell tolerance induction	0.0383792	ENSBTAG00000019832
27	GO:BP	GO:0003149	membranous septum morphogenesis	0.0383792	ENSBTAG00000019832
28	GO:BP	GO:1990086	lens fiber cell apoptotic process	0.0383792	ENSBTAG00000019832
29	GO:BP	GO:0002520	immune system development	0.0383792	ENSBTAG00000019832,ENSBTAG00000019194
30	GO:BP	GO:0002666	positive regulation of T cell tolerance induction	0.0383792	ENSBTAG00000019832
31	GO:BP	GO:0002651	positive regulation of tolerance induction to self antigen	0.0383792	ENSBTAG00000019832
32	GO:BP	GO:0061343	cell adhesion involved in heart morphogenesis	0.0383792	ENSBTAG00000019832
33	GO:BP	GO:0002649	regulation of tolerance induction to self antigen	0.0383792	ENSBTAG00000019832
34	GO:BP	GO:0002661	regulation of B cell tolerance induction	0.0383792	ENSBTAG00000019832
35	GO:BP	GO:0002664	regulation of T cell tolerance induction	0.0383792	ENSBTAG00000019832
36	GO:BP	GO:0002663	positive regulation of B cell tolerance induction	0.0383792	ENSBTAG00000019832
37	GO:BP	GO:0002684	positive regulation of immune system process	0.0428818	ENSBTAG00000019832,ENSBTAG00000019872,ENSBTAG00000019194
38	GO:BP	GO:0048630	skeletal muscle tissue growth	0.0431888	ENSBTAG00000011743
39	GO:BP	GO:0051251	positive regulation of lymphocyte activation	0.0431888	ENSBTAG00000019832,ENSBTAG00000019194
40	GO:BP	GO:0034154	toll-like receptor 7 signaling pathway	0.0431888	ENSBTAG00000019872
41	GO:BP	GO:0060439	trachea morphogenesis	0.0431888	ENSBTAG00000019832
42	GO:BP	GO:0002517	T cell tolerance induction	0.0431888	ENSBTAG00000019832
43	GO:BP	GO:0002645	positive regulation of tolerance induction	0.0431888	ENSBTAG00000019832
44	GO:BP	GO:0060433	bronchus development	0.0460331	ENSBTAG00000019832
45	GO:BP	GO:0003418	growth plate cartilage chondrocyte differentiation	0.0460331	ENSBTAG00000019832
46	GO:BP	GO:0002696	positive regulation of leukocyte activation	0.0482941	ENSBTAG00000019832,ENSBTAG00000019194
47	GO:BP	GO:0001865	NK T cell differentiation	0.0489635	ENSBTAG00000019832
48	GO:BP	GO:0050867	positive regulation of cell activation	0.0496277	ENSBTAG00000019832,ENSBTAG00000019194
49	GO:BP	GO:0003198	epithelial to mesenchymal transition involved in endocardial cushion formation	0.0496277	ENSBTAG00000019832
50	GO:BP	GO:0002643	regulation of tolerance induction	0.0496277	ENSBTAG00000019832
51	GO:BP	GO:2001034	positive regulation of double-strand break repair via nonhomologous end joining	0.0496277	ENSBTAG00000019194
52	GO:MF	GO:0004352	glutamate dehydrogenase (NAD+) activity	0.0098128	ENSBTAG00000007540
53	GO:MF	GO:0004353	glutamate dehydrogenase [NAD(P)+] activity	0.0098128	ENSBTAG00000007540
54	GO:MF	GO:0016639	oxidoreductase activity, acting on the CH-NH2 group of donors, NAD or NADP as acceptor	0.0098128	ENSBTAG00000007540
55	GO:MF	GO:0005026	transforming growth factor beta receptor activity, type II	0.0147171	ENSBTAG00000019832
56	GO:MF	GO:0048185	activin binding	0.0326677	ENSBTAG00000019832
57	GO:MF	GO:0036312	phosphatidylinositol 3-kinase regulatory subunit binding	0.0326677	ENSBTAG00000019872
58	GO:MF	GO:0034713	type I transforming growth factor beta receptor binding	0.0326677	ENSBTAG00000019832
59	GO:MF	GO:0017002	activin receptor activity	0.0326677	ENSBTAG00000019832
60	GO:MF	GO:0005024	transforming growth factor beta receptor activity	0.0326677	ENSBTAG00000019832
61	GO:MF	GO:0050431	transforming growth factor beta binding	0.0461234	ENSBTAG00000019832
62	GO:MF	GO:0043548	phosphatidylinositol 3-kinase binding	0.0461234	ENSBTAG00000019872
63	GO:MF	GO:0016638	oxidoreductase activity, acting on the CH-NH2 group of donors	0.0461234	ENSBTAG00000007540
64	GO:MF	GO:0005160	transforming growth factor beta receptor binding	0.0461234	ENSBTAG00000019832
65	GO:MF	GO:0004675	transmembrane receptor protein serine/threonine kinase activity	0.0461234	ENSBTAG00000019832
66	REAC	REAC:R-BTA-2243919	Crosslinking of collagen fibrils	0.0440084	ENSBTAG00000011743
67	REAC	REAC:R-BTA-2173788	Downregulation of TGF-beta receptor signaling	0.0440084	ENSBTAG00000019832
68	REAC	REAC:R-BTA-8964539	Glutamate and glutamine metabolism	0.0440084	ENSBTAG00000007540
69	REAC	REAC:R-BTA-112308	Presynaptic depolarization and calcium channel opening	0.0440084	ENSBTAG00000013117
70	REAC	REAC:R-BTA-2151201	Transcriptional activation of mitochondrial biogenesis	0.0440084	ENSBTAG00000007540
71	REAC	REAC:R-BTA-2173791	TGF-beta receptor signaling in EMT (epithelial to mesenchymal transition)	0.0440084	ENSBTAG00000019832

13.1 GPROFILER ON-LINE

From the online version of GPROFILER i got the following results.

Legend

Additionaly I perfomed a another version of this analysis but with no eletronic GO annotation

13.2 Terms distribution Pie Chart

Now I’m gonna make a pie chart with the categories

library(ggplot2)

# Assuming sig_enrich$Category contains the categories
category_counts <- table(result_enrich$Category)

# Create a pie chart
pie_chart <- ggplot(data = NULL, aes(x = factor(1), fill = names(category_counts), y = as.numeric(category_counts))) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar(theta = "y") +
  labs(title = "Distribution among terms",
       x = NULL,
       y = NULL,
       fill = "Category") +
  theme_void() +
  theme(legend.position = "right")

# Print the pie chart
print(pie_chart)

14 Haplotype Blocks

First I need to generate the .ped file

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean
RESULT=/home/bambrozi/2_CORTISOL/HAPLOBLOCK/inter_file

plink \
    --bfile ${DATA} \
    --cow \
    --recode \
    --out ${RESULT}

Second, is necessary to convert the .ped file to Linkage format:

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/HAPLOBLOCK/inter_file
RESULT=/home/bambrozi/2_CORTISOL/HAPLOBLOCK/hapblock_in

plink \
    --file ${DATA} \
    --cow \
    --recode HV \
    --out ${RESULT}

The code above will generate a pair of files .ped and .info for each chromosome

I ran the haploview only for chromosome 11, 22, 26, 28 that are when the significant SNP are located.

As the LD Plot bring the SNP ordered, first I looked at /home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg.txt, to know the SNP position, and then compare with the Check Markers tab in the haploview SNP position x SNP# in plot

Chr	SNP	bp	A1	A2	Freq	Haploview.order
11	BTB-01060598	32301594	C	A	0.4503970	680
22	ARS-BFGL-NGS-23411	5134078	A	C	0.1865080	88
22	ARS-BFGL-NGS-26419	46052082	A	C	0.0813492	762
26	BTB-00926636	17857480	C	A	0.0257937	296
26	BTA-62125-no-rs	17899619	C	A	0.0257937	297
28	BTA-99379-no-rs	41666186	A	C	0.0496032	701

The two significant SNPs from chromosome 26 fell within a Haploblock.

Chr 11 (SNP 680)

Chr 22 (SNP 88)

Chr 22 (SNP 762)

Chr 26 (SNP 296 and 297)

Chr 28 (SNP 701)

14.1 BLOCK 10 in Chr 26

As we found an haploblock on chromosome 26 I decided take a look inside this block. So, on Haploview I checked the name and position of the first SNP of this block (292) and the last (300).

X.	Name	Position	ObsHET	PredHET	HWpval	X.Geno	FamTrio	MendErr	MAF	Alleles
292	ARS-BFGL-NGS-111577	17714604	0.401	0.448	0.1161	100	0	0	0.339	A:C
293	ARS-BFGL-NGS-21408	17746468	0.369	0.403	0.2260	100	0	0	0.280	C:A
294	ARS-BFGL-NGS-16328	17778908	0.056	0.054	1.0000	100	0	0	0.028	A:C
295	ARS-BFGL-NGS-117063	17817225	0.413	0.452	0.2022	100	0	0	0.345	C:A
296	BTB-00926636	17857480	0.052	0.050	1.0000	100	0	0	0.026	A:C
297	BTA-62125-no-rs	17899619	0.052	0.050	1.0000	100	0	0	0.026	A:C
298	ARS-BFGL-NGS-110679	17953795	0.397	0.433	0.2211	100	0	0	0.317	A:C
299	ARS-BFGL-NGS-72889	17989430	0.052	0.050	1.0000	100	0	0	0.026	C:A
300	BTB-00926868	18040673	0.385	0.420	0.2319	100	0	0	0.300	A:C

Then I checked on NCBI’s Genome Data Viewer which genes are inside this block, adding 50k before the first SNP and 50K after the last SNP.

This interval has 426,069bp and has this genes inside:

• DNTT
• OPALIN
• TLL2
• TM9SF3
• PIK3AP1

As you can see in the image below:

The genes TLL2, TM9SF3 and PIK3AP1 already were in the gene list from GALLO, but the genes DNTT and OPALIN were new!

I performed a GPROFILER analysis only for these genes spotted in this Block 10.

15 5 - 15%

After check the outcome for 5% of significance for SNPs, Prof. Flávio asked me to carry out again considering the interval between 5% and 15% of significance.

15.1 Corrected Bonferroni for genome independent segments

Now we are going to apply a correction for multiple testing modifying the Bonferroni test adjusting not for the total number of SNPs but for the number of the independent segments in the genome.

Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)

# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8

# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)

NeL <- Ne*L_M

# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)


gwas<- read.table(file = "/home/bambrozi/2_CORTISOL/GWAS/GWAS_result.loco.mlma", 
                  head=T, stringsAsFactors = F)

gwas$Chr<- as.factor(gwas$Chr)
gwas$logP<- -log10(gwas$p)
rmv<- which(gwas$logP == "NaN")
if (length(rmv) >=1) {gwas <- gwas[-rmv,]}

bonf05<- -log10(0.05/Me)
bonf15<- -log10(0.15/Me)

library(GHap)
ghap.manhattan(data=gwas,chr="Chr", bp="bp", y="logP", type="p", pch = 20, 
               cex=1, lwd=1, ylab="", xlab="Chromossomes", 
               main="GWAS Cortisol", backcolor="#F5EFE780", chr.ang=0,)
abline(h=(bonf05), col="black")
abline(h=(bonf15), col="red2")
legend("topleft", inset=c(0.05, 0.05), col="red2", lwd=2, 
       legend=c("15% sig. Bonferroni corr. for ind. segments"), bty="n")
legend("topleft", inset=c(0.05, 0.10), col="black", lwd=2, 
       legend=c("5% sig. Bonferroni corr. for ind. segments"), bty="n")

library(tidyverse)

#Recovering the significant SNPs > <5%
SNP_sig_bonf_ind_seg_5 <- filter(gwas, logP > bonf05)
write.csv(SNP_sig_bonf_ind_seg_5, file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg_5.csv")


#Recovering the significant SNPs > 15% and <5%
SNP_sig_bonf_ind_seg_5_15 <- filter(gwas, logP > bonf15 & logP < bonf05)
write.csv(SNP_sig_bonf_ind_seg_5_15, file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg_5_15.csv")


#Recovering the significant SNPs > 15% 
SNP_sig_bonf_ind_seg_15 <- filter(gwas, logP > bonf15)
write.csv(SNP_sig_bonf_ind_seg_15, file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg_15.csv")

Below we can find the list of significant SNPs after the correction for Multiple Testing

Chr	X	X15.	X5.	X5.15.	logP
28	BTA-99379-no-rs	X	X		6.610970
22	ARS-BFGL-NGS-23411	X	X		5.485541
22	ARS-BFGL-NGS-26419	X	X		5.272074
26	BTA-62125-no-rs	X	X		4.732155
26	BTB-00926636	X	X		4.732155
11	BTB-01060598	X	X		4.682532
20	BTB-01471673	X		X	4.182974
23	Hapmap54016-rs29022819	X		X	4.016453
28	ARS-BFGL-NGS-11935	X		X	3.986177
28	Hapmap43005-BTA-64606	X		X	3.986177
11	BTA-122098-no-rs	X		X	3.980028
19	ARS-BFGL-NGS-29119	X		X	3.925461
14	Hapmap39823-BTA-35254	X		X	3.905277
19	ARS-BFGL-NGS-28901	X		X	3.888442
3	ARS-BFGL-NGS-4585	X		X	3.885943
14	UA-IFASA-7664	X		X	3.882503

To make easier to check the name of the significant SNPs I made the following Manhattan Plot

# Prepare the dataset
don <- gwas %>% 
  group_by(Chr) %>% 
  summarise(chr_len = max(bp)) %>% 
  mutate(tot = cumsum(as.numeric(chr_len)) - as.numeric(chr_len)) %>%
  select(-chr_len) %>%
  left_join(gwas, ., by = c("Chr" = "Chr")) %>%
  arrange(Chr, bp) %>%
  mutate(BPcum = as.numeric(bp) + tot) %>%
  mutate(is_highlight = ifelse(logP > bonf15, "yes", "no")) %>%
  mutate(is_annotate = ifelse(logP > bonf15, "yes", "no"))

# Remove rows with missing BPcum values
don <- don %>% filter(!is.na(BPcum))

# Prepare X axis
axisdf <- don %>% 
  group_by(Chr) %>% 
  summarize(center = (max(BPcum) + min(BPcum)) / 2)

# Find the maximum logP value for setting y-axis limits
max_logP <- max(don$logP, na.rm = TRUE)

# Make the plot
ggplot(don, aes(x = BPcum, y = logP)) +
  geom_point(aes(color = as.factor(Chr)), alpha = 0.8, size = 1.3) +
  scale_color_manual(values = rep(c("grey", "skyblue"), 22)) +
  scale_x_continuous(label = axisdf$Chr, breaks = axisdf$center) +
  scale_y_continuous(expand = c(0, 0), limits = c(0, max_logP + 1)) + # Set y-axis limit
  geom_hline(yintercept = bonf05, color = "black") +  # Add horizontal line for bonf05
  geom_hline(yintercept = bonf15, color = "red2") +   # Add horizontal line for bonf15
  geom_point(data = subset(don, is_highlight == "yes"), color = "orange", size = 2) +
  geom_text_repel(data = subset(don, is_annotate == "yes"), aes(label = SNP), size = 2) +
  annotate("text", x = Inf, y = Inf, label = "15% sig. Bonferroni corr. for ind. segments", 
           color = "red2", hjust = 1.1, vjust = 2, size = 3) +
  annotate("text", x = Inf, y = Inf, label = "5% sig. Bonferroni corr. for ind. segments", 
           color = "black", hjust = 1.1, vjust = 3.5, size = 3) +
  theme_bw() +
  theme(
    legend.position = "none",
    panel.border = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  labs(title = "GWAS Cortisol", x = "Chromosomes", y = "-log10(p)")

Generating a file with the GWAS output and rsID

library(tidyverse)

# Extracting SNP names to use in the SNPChimp
SNP_NAMES <- SNP_sig_bonf_ind_seg_15 %>% pull(SNP)
SNP_NAMES <- paste(SNP_NAMES, collapse = ", ")
print(SNP_NAMES)

# Copy this printed names and paste on SNPChimp

# Opening the SNPChimp output in R
rsID <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/SNPchimp_result_ind_seg_15.tsv")
rsID <- rsID[,c("rs","SNP_name")]

#Merging in one file the GWAS output and rsID
SNP_rsID_sig_bonf_ind_seg_15 <- merge(SNP_sig_bonf_ind_seg_15, rsID, by.x = "SNP", by.y = "SNP_name")
SNP_rsID_sig_bonf_ind_seg_15 <- SNP_rsID_sig_bonf_ind_seg_15[, c("rs", "SNP", "Chr", "bp", "A1", "A2", "Freq", "b", "se", "p", "logP")]
write.csv(SNP_rsID_sig_bonf_ind_seg_15, "/home/bambrozi/2_CORTISOL/GWAS/SNP_rsID_bonf_ind_seg_15.csv")

Bellow we can see the table with the SNP names and rsID for all significant SNPs with p-value lower than 0,15.

X	rs	SNP	Chr	bp	A1	A2	Freq	b	se	p	logP
1	rs109578343	ARS-BFGL-NGS-11935	28	44942879	A	C	0.0297619	412.261	106.1750	0.0001032	3.986177
2	rs110031217	ARS-BFGL-NGS-23411	22	5134078	A	C	0.1865080	207.116	44.5110	0.0000033	5.485541
3	rs110991998	ARS-BFGL-NGS-26419	22	46052082	A	C	0.0813492	308.357	67.7592	0.0000053	5.272074
4	rs109751680	ARS-BFGL-NGS-28901	19	42338148	C	A	0.3710320	141.156	36.8765	0.0001293	3.888442
5	rs41577586	ARS-BFGL-NGS-29119	19	49744592	A	C	0.4047620	-142.084	36.9169	0.0001187	3.925461
6	rs109888380	ARS-BFGL-NGS-4585	3	107160292	A	C	0.3353170	-144.488	37.7607	0.0001300	3.885943
7	rs41624254	BTA-122098-no-rs	11	31882807	A	C	0.4444440	131.779	33.9688	0.0001047	3.980028
8	rs41644634	BTA-62125-no-rs	26	17899619	C	A	0.0257937	487.149	113.7690	0.0000185	4.732155
9	rs41567074	BTA-99379-no-rs	28	41666186	A	C	0.0496032	435.809	84.4338	0.0000002	6.610970
10	rs42089058	BTB-00926636	26	17857480	C	A	0.0257937	487.149	113.7690	0.0000185	4.732155
11	rs42217767	BTB-01060598	11	32301594	C	A	0.4503970	-152.746	35.8860	0.0000208	4.682532
12	rs42590312	BTB-01471673	20	11545685	A	C	0.2460320	169.707	42.5157	0.0000656	4.182974
13	rs41632193	Hapmap39823-BTA-35254	14	64061208	A	C	0.2916670	-138.029	35.9698	0.0001244	3.905277
14	rs41648479	Hapmap43005-BTA-64606	28	45805105	A	C	0.0297619	412.261	106.1750	0.0001032	3.986177
15	rs29022819	Hapmap54016-rs29022819	23	33609998	C	A	0.1904760	173.228	44.4201	0.0000963	4.016453
16	rs41632223	UA-IFASA-7664	14	64299714	C	A	0.2678570	-141.443	36.9840	0.0001311	3.882503

16 qqPlot

17 VEP - Variant Effect Prediction

After insert all rsID on VEP the summary is shown below:

As can be seen, the VEP only analysed 14 variants

Bellow I’m gonna show the “genome view” for each SNP (variant) recovered from VEP:

rs41648479

rs41632223

rs41632193

rs42590312

rs42089058

rs41567074

rs41644634

rs41624254

rs109888380

rs41577586

rs109751680

rs110991998

rs110031217

rs109578343

ps. the unique reference genome available in the VEP is the ARS-UCD1.3, which is not a problem once it is working with the rsID, which for sure is our variant.

18 GALLO

# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#import MARKER files = the GWAS output
gwas <- read.csv(file = "/home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg_15.csv")

# Assuming "gwas" is your dataframe
gwas <- subset(gwas, select = c(Chr, SNP, bp))


colnames(gwas) <- c("CHR","SNP", "BP")


#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.table(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/Bonf_5_15/out_genes_50k_15.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/Bonf_5_15/out_genes_50k_15.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/Bonf_5_15/out_qtl_50k_15.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("CHR", "SNP", "BP", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/Bonf_5_15/out_qtl_50k_clean_15.csv")

Dowloading the .gtf file from Ensembl https://useast.ensembl.org/info/data/ftp/index.html

Downloading the .gff file from AnimalQTLdb https://www.animalgenome.org/cgi-bin/QTLdb/index

The GALLO output are bellow:

For GENES

X	CHR	SNP	BP	chr	start_pos	end_pos	width	strand	gene_id	gene_name	gene_biotype
1	3	ARS-BFGL-NGS-4585	107160292	3	107109148	107110689	1542	+	ENSBTAG00000030338	GJA9	protein_coding
2	3	ARS-BFGL-NGS-4585	107160292	3	107111822	107117831	6010	+	ENSBTAG00000030337	MYCBP	protein_coding
3	3	ARS-BFGL-NGS-4585	107160292	3	107121920	107136338	14419	+	ENSBTAG00000009368	RRAGC	protein_coding
4	11	BTB-01060598	32301594	11	32278324	32766620	488297	-	ENSBTAG00000046199	NRXN1	protein_coding
5	11	BTB-01060598	32301594	11	32280416	32280512	97	-	ENSBTAG00000044573	U6	snRNA
6	14	Hapmap39823-BTA-35254	64061208	14	64047857	64103385	55529	+	ENSBTAG00000017833	RNF19A	protein_coding
7	14	UA-IFASA-7664	64299714	14	64289853	64398324	108472	+	ENSBTAG00000019793	RGS22	protein_coding
8	19	ARS-BFGL-NGS-29119	49744592	19	49662140	49766195	104056	+	ENSBTAG00000052072	B3GNTL1	protein_coding
9	19	ARS-BFGL-NGS-29119	49744592	19	49768681	49927209	158529	-	ENSBTAG00000015414	TBCD	protein_coding
10	19	ARS-BFGL-NGS-28901	42338148	19	42283857	42299780	15924	-	ENSBTAG00000001195	KCNH4	protein_coding
11	19	ARS-BFGL-NGS-28901	42338148	19	42302320	42303571	1252	-	ENSBTAG00000000665	HCRT	protein_coding
12	19	ARS-BFGL-NGS-28901	42338148	19	42307188	42312293	5106	-	ENSBTAG00000000666	GHDC	protein_coding
13	19	ARS-BFGL-NGS-28901	42338148	19	42319170	42357910	38741	-	ENSBTAG00000010125	STAT5B	protein_coding
14	19	ARS-BFGL-NGS-28901	42338148	19	42387164	42390885	3722	+	ENSBTAG00000052763	NA	lncRNA
15	22	ARS-BFGL-NGS-26419	46052082	22	45924535	46818511	893977	-	ENSBTAG00000013117	CACNA2D3	protein_coding
16	22	ARS-BFGL-NGS-26419	46052082	22	46051998	46062657	10660	+	ENSBTAG00000013124	LRTM1	protein_coding
17	22	ARS-BFGL-NGS-23411	5134078	22	5091037	5184321	93285	+	ENSBTAG00000019832	TGFBR2	protein_coding
18	23	Hapmap54016-rs29022819	33609998	23	33618285	33618377	93	+	ENSBTAG00000045279	SNORA70	snoRNA
19	26	BTB-00926636	17857480	26	17703169	17846407	143239	-	ENSBTAG00000011743	TLL2	protein_coding
20	26	BTB-00926636	17857480	26	17851539	17912665	61127	-	ENSBTAG00000016298	TM9SF3	protein_coding
21	26	BTA-62125-no-rs	17899619	26	17851539	17912665	61127	-	ENSBTAG00000016298	TM9SF3	protein_coding
22	26	BTA-62125-no-rs	17899619	26	17925643	18029878	104236	-	ENSBTAG00000019872	PIK3AP1	protein_coding
23	28	BTA-99379-no-rs	41666186	28	41609015	41649016	40002	-	ENSBTAG00000007540	GLUD1	protein_coding
24	28	BTA-99379-no-rs	41666186	28	41650333	41763371	113039	+	ENSBTAG00000019194	SHLD2	protein_coding
25	28	Hapmap43005-BTA-64606	45805105	28	45757443	45768564	11122	+	ENSBTAG00000012393	AGT	protein_coding
26	28	Hapmap43005-BTA-64606	45805105	28	45772443	45814853	42411	-	ENSBTAG00000020314	COG2	protein_coding

FOR QTLs

X	CHR	SNP	BP	QTL_type	start_pos	end_pos	QTL_ID
1	3	ARS-BFGL-NGS-4585	107160292	Reproduction	107191525	107191529	40198
2	3	ARS-BFGL-NGS-4585	107160292	Exterior	107191525	107191529	40199
3	3	ARS-BFGL-NGS-4585	107160292	Reproduction	107191525	107191529	40200
4	3	ARS-BFGL-NGS-4585	107160292	Exterior	107191525	107191529	40201
5	3	ARS-BFGL-NGS-4585	107160292	Milk	107191525	107191529	40202
6	3	ARS-BFGL-NGS-4585	107160292	Milk	107191525	107191529	40203
7	3	ARS-BFGL-NGS-4585	107160292	Production	107191525	107191529	40204
8	3	ARS-BFGL-NGS-4585	107160292	Production	107191525	107191529	40205
9	3	ARS-BFGL-NGS-4585	107160292	Milk	107191525	107191529	40206
10	3	ARS-BFGL-NGS-4585	107160292	Milk	107191525	107191529	40207
11	3	ARS-BFGL-NGS-4585	107160292	Exterior	107191525	107191529	40208
12	3	ARS-BFGL-NGS-4585	107160292	Reproduction	107191525	107191529	40209
13	3	ARS-BFGL-NGS-4585	107160292	Health	107191525	107191529	40210
14	3	ARS-BFGL-NGS-4585	107160292	Reproduction	107191525	107191529	40211
15	3	ARS-BFGL-NGS-4585	107160292	Exterior	107191525	107191529	40212
16	3	ARS-BFGL-NGS-4585	107160292	Exterior	107191525	107191529	40213
17	11	BTA-122098-no-rs	31882807	Milk	31870044	31870048	122275
18	14	Hapmap39823-BTA-35254	64061208	Milk	64013785	64013789	247433
19	14	Hapmap39823-BTA-35254	64061208	Milk	64013858	64013862	241580
20	14	Hapmap39823-BTA-35254	64061208	Milk	64013858	64013862	252031
21	14	Hapmap39823-BTA-35254	64061208	Milk	64013897	64013901	241079
22	14	Hapmap39823-BTA-35254	64061208	Milk	64013897	64013901	251434
23	14	Hapmap39823-BTA-35254	64061208	Milk	64013952	64013956	241853
24	14	Hapmap39823-BTA-35254	64061208	Milk	64013952	64013956	252403
25	14	Hapmap39823-BTA-35254	64061208	Milk	64015726	64015730	105628
26	14	Hapmap39823-BTA-35254	64061208	Milk	64017424	64017428	252151
27	14	Hapmap39823-BTA-35254	64061208	Milk	64017970	64017974	18873
28	14	Hapmap39823-BTA-35254	64061208	Milk	64061206	64061210	18872
29	14	Hapmap39823-BTA-35254	64061208	Milk	64061206	64061210	105335
30	14	Hapmap39823-BTA-35254	64061208	Milk	64061206	64061210	173792
31	14	Hapmap39823-BTA-35254	64061208	Milk	64061206	64061210	175101
32	14	Hapmap39823-BTA-35254	64061208	Milk	64061206	64061210	176088
33	14	Hapmap39823-BTA-35254	64061208	Milk	64061206	64061210	176487
34	14	Hapmap39823-BTA-35254	64061208	Milk	64061206	64061210	252723
35	14	Hapmap39823-BTA-35254	64061208	Milk	64063422	64063426	175102
36	14	Hapmap39823-BTA-35254	64061208	Milk	64068329	64068333	105336
37	14	Hapmap39823-BTA-35254	64061208	Milk	64081490	64081494	104637
38	14	Hapmap39823-BTA-35254	64061208	Milk	64081490	64081494	104890
39	14	Hapmap39823-BTA-35254	64061208	Health	64081490	64081494	179830
40	14	Hapmap39823-BTA-35254	64061208	Milk	64087234	64087238	175103
41	14	Hapmap39823-BTA-35254	64061208	Milk	64087234	64087238	252869
42	14	Hapmap39823-BTA-35254	64061208	Milk	64087450	64087454	252628
43	14	Hapmap39823-BTA-35254	64061208	Milk	64088569	64088573	252926
44	14	Hapmap39823-BTA-35254	64061208	Milk	64103583	64103587	104638
45	14	Hapmap39823-BTA-35254	64061208	Milk	64103583	64103587	104891
46	14	UA-IFASA-7664	64299714	Milk	64272869	64272873	102476
47	14	UA-IFASA-7664	64299714	Milk	64272869	64272873	104484
48	14	UA-IFASA-7664	64299714	Milk	64272869	64272873	104863
49	14	UA-IFASA-7664	64299714	Health	64272869	64272873	179764
50	14	UA-IFASA-7664	64299714	Milk	64283263	64283267	175108
51	14	UA-IFASA-7664	64299714	Milk	64283263	64283267	251800
52	14	UA-IFASA-7664	64299714	Milk	64283947	64283951	251398
53	14	UA-IFASA-7664	64299714	Milk	64288968	64288972	247633
54	14	UA-IFASA-7664	64299714	Milk	64288968	64288972	252142
55	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	18875
56	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	105660
57	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	158137
58	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	173793
59	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	175109
60	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	176085
61	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	176486
62	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	250009
63	14	UA-IFASA-7664	64299714	Milk	64299712	64299716	252724
64	14	UA-IFASA-7664	64299714	Milk	64302757	64302761	251934
65	14	UA-IFASA-7664	64299714	Milk	64308608	64308612	252948
66	14	UA-IFASA-7664	64299714	Milk	64317001	64317005	252652
67	14	UA-IFASA-7664	64299714	Milk	64322496	64322500	251752
68	14	UA-IFASA-7664	64299714	Meat_and_Carcass	64327478	64327482	20269
69	14	UA-IFASA-7664	64299714	Meat_and_Carcass	64327478	64327482	20311
70	14	UA-IFASA-7664	64299714	Meat_and_Carcass	64327478	64327482	20393
71	14	UA-IFASA-7664	64299714	Meat_and_Carcass	64327478	64327482	20464
72	14	UA-IFASA-7664	64299714	Milk	64327904	64327908	32596
73	14	UA-IFASA-7664	64299714	Milk	64327904	64327908	32634
74	14	UA-IFASA-7664	64299714	Milk	64328322	64328326	252206
75	14	UA-IFASA-7664	64299714	Milk	64339118	64339122	251497
76	14	UA-IFASA-7664	64299714	Milk	64343288	64343292	252287
77	19	ARS-BFGL-NGS-28901	42338148	Milk	42292941	42292945	147657
78	19	ARS-BFGL-NGS-28901	42338148	Milk	42292941	42292945	147675
79	19	ARS-BFGL-NGS-28901	42338148	Milk	42322679	42322683	173073
80	19	ARS-BFGL-NGS-28901	42338148	Milk	42322679	42322683	173074
81	19	ARS-BFGL-NGS-28901	42338148	Milk	42322679	42322683	173075
82	19	ARS-BFGL-NGS-28901	42338148	Milk	42322679	42322683	173076
83	19	ARS-BFGL-NGS-28901	42338148	Milk	42322679	42322683	173077
84	19	ARS-BFGL-NGS-28901	42338148	Milk	42322679	42322683	173237
85	19	ARS-BFGL-NGS-28901	42338148	Milk	42338146	42338150	166545
86	19	ARS-BFGL-NGS-28901	42338148	Milk	42338146	42338150	166546
87	19	ARS-BFGL-NGS-28901	42338148	Milk	42338146	42338150	253605
88	19	ARS-BFGL-NGS-28901	42338148	Milk	42349650	42349654	147630
89	19	ARS-BFGL-NGS-29119	49744592	Milk	49702578	49702582	195323
90	19	ARS-BFGL-NGS-29119	49744592	Milk	49702578	49702582	195324
91	19	ARS-BFGL-NGS-29119	49744592	Milk	49702578	49702582	195325
92	19	ARS-BFGL-NGS-29119	49744592	Milk	49702578	49702582	205408
93	19	ARS-BFGL-NGS-29119	49744592	Meat_and_Carcass	49715846	49715850	107754
94	19	ARS-BFGL-NGS-29119	49744592	Milk	49735877	49735881	196362
95	19	ARS-BFGL-NGS-29119	49744592	Milk	49772086	49772090	195342
96	20	BTB-01471673	11545685	Production	11498161	11498165	65554
97	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5109498	5109502	151609
98	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5134076	5134080	151620
99	22	ARS-BFGL-NGS-23411	5134078	Meat_and_Carcass	5173197	5173201	151601
100	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51802
101	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51803
102	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51804
103	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51805
104	22	ARS-BFGL-NGS-26419	46052082	Production	46052080	46052084	51806
105	22	ARS-BFGL-NGS-26419	46052082	Production	46052080	46052084	51807
106	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51808
107	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51809
108	22	ARS-BFGL-NGS-26419	46052082	Health	46052080	46052084	51810
109	22	ARS-BFGL-NGS-26419	46052082	Reproduction	46052080	46052084	51811
110	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51812
111	22	ARS-BFGL-NGS-26419	46052082	Exterior	46052080	46052084	51813
112	22	ARS-BFGL-NGS-26419	46052082	Milk	46073517	46073521	243365
113	26	BTB-00926636	17857480	Milk	17809768	17809772	195264
114	26	BTB-00926636	17857480	Milk	17809768	17809772	200269
115	26	BTB-00926636	17857480	Milk	17809768	17809772	200270
116	26	BTB-00926636	17857480	Milk	17809768	17809772	205295
117	26	BTB-00926636	17857480	Milk	17810363	17810367	198859
118	26	BTB-00926636	17857480	Milk	17810363	17810367	203868
119	26	BTB-00926636	17857480	Milk	17810363	17810367	203869
120	26	BTB-00926636	17857480	Milk	17810363	17810367	210297
121	26	BTB-00926636	17857480	Milk	17811021	17811025	198858
122	26	BTB-00926636	17857480	Milk	17811021	17811025	203866
123	26	BTB-00926636	17857480	Milk	17811021	17811025	203867
124	26	BTB-00926636	17857480	Milk	17811021	17811025	210296
125	26	BTB-00926636	17857480	Milk	17811725	17811729	197493
126	26	BTB-00926636	17857480	Milk	17811725	17811729	202704
127	26	BTB-00926636	17857480	Milk	17812273	17812277	195685
128	26	BTB-00926636	17857480	Milk	17812273	17812277	200785
129	26	BTB-00926636	17857480	Milk	17813212	17813216	195285
130	26	BTB-00926636	17857480	Milk	17813212	17813216	200301
131	26	BTB-00926636	17857480	Milk	17813978	17813982	196236
132	26	BTB-00926636	17857480	Milk	17813978	17813982	201468
133	26	BTB-00926636	17857480	Milk	17815237	17815241	198002
134	26	BTB-00926636	17857480	Milk	17815237	17815241	203190
135	26	BTB-00926636	17857480	Milk	17817223	17817227	34750
136	26	BTB-00926636	17857480	Milk	17817223	17817227	195586
137	26	BTB-00926636	17857480	Milk	17821297	17821301	197235
138	26	BTB-00926636	17857480	Milk	17821297	17821301	202455
139	26	BTB-00926636	17857480	Milk	17822209	17822213	197783
140	26	BTB-00926636	17857480	Milk	17822209	17822213	202983
141	26	BTB-00926636	17857480	Milk	17823105	17823109	196204
142	26	BTB-00926636	17857480	Milk	17823105	17823109	201419
143	26	BTB-00926636	17857480	Milk	17824770	17824774	196867
144	26	BTB-00926636	17857480	Milk	17824770	17824774	202101
145	26	BTB-00926636	17857480	Milk	17833551	17833555	195748
146	26	BTB-00926636	17857480	Milk	17833551	17833555	200855
147	26	BTB-00926636	17857480	Milk	17833551	17833555	206213
148	26	BTB-00926636	17857480	Milk	17865069	17865073	197144
149	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	197144
150	26	BTB-00926636	17857480	Milk	17865069	17865073	202367
151	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	202367
152	26	BTB-00926636	17857480	Milk	17865069	17865073	208485
153	26	BTA-62125-no-rs	17899619	Milk	17865069	17865073	208485
154	26	BTB-00926636	17857480	Milk	17871201	17871205	197611
155	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	197611
156	26	BTB-00926636	17857480	Milk	17871201	17871205	202816
157	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	202816
158	26	BTB-00926636	17857480	Milk	17871201	17871205	209076
159	26	BTA-62125-no-rs	17899619	Milk	17871201	17871205	209076
160	26	BTB-00926636	17857480	Milk	17885871	17885875	63656
161	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	63656
162	26	BTB-00926636	17857480	Milk	17885871	17885875	199068
163	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	199068
164	26	BTB-00926636	17857480	Milk	17885871	17885875	204113
165	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	204113
166	26	BTB-00926636	17857480	Milk	17885871	17885875	204114
167	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	204114
168	26	BTB-00926636	17857480	Milk	17885871	17885875	210399
169	26	BTA-62125-no-rs	17899619	Milk	17885871	17885875	210399
170	26	BTB-00926636	17857480	Milk	17895273	17895277	63657
171	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	63657
172	26	BTB-00926636	17857480	Milk	17895273	17895277	199069
173	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	199069
174	26	BTB-00926636	17857480	Milk	17895273	17895277	204115
175	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	204115
176	26	BTB-00926636	17857480	Milk	17895273	17895277	204116
177	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	204116
178	26	BTB-00926636	17857480	Milk	17895273	17895277	210400
179	26	BTA-62125-no-rs	17899619	Milk	17895273	17895277	210400
180	26	BTB-00926636	17857480	Milk	17895803	17895807	199070
181	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	199070
182	26	BTB-00926636	17857480	Milk	17895803	17895807	204117
183	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	204117
184	26	BTB-00926636	17857480	Milk	17895803	17895807	204118
185	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	204118
186	26	BTB-00926636	17857480	Milk	17895803	17895807	210401
187	26	BTA-62125-no-rs	17899619	Milk	17895803	17895807	210401
188	26	BTB-00926636	17857480	Milk	17900304	17900308	199071
189	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	199071
190	26	BTB-00926636	17857480	Milk	17900304	17900308	204119
191	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	204119
192	26	BTB-00926636	17857480	Milk	17900304	17900308	204120
193	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	204120
194	26	BTB-00926636	17857480	Milk	17900304	17900308	210402
195	26	BTA-62125-no-rs	17899619	Milk	17900304	17900308	210402
196	26	BTB-00926636	17857480	Milk	17902932	17902936	63658
197	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	63658
198	26	BTB-00926636	17857480	Milk	17902932	17902936	199072
199	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	199072
200	26	BTB-00926636	17857480	Milk	17902932	17902936	204121
201	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	204121
202	26	BTB-00926636	17857480	Milk	17902932	17902936	204122
203	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	204122
204	26	BTB-00926636	17857480	Milk	17902932	17902936	210403
205	26	BTA-62125-no-rs	17899619	Milk	17902932	17902936	210403
206	26	BTB-00926636	17857480	Milk	17906861	17906865	63660
207	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	63660
208	26	BTB-00926636	17857480	Milk	17906861	17906865	199074
209	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	199074
210	26	BTB-00926636	17857480	Milk	17906861	17906865	204125
211	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	204125
212	26	BTB-00926636	17857480	Milk	17906861	17906865	204126
213	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	204126
214	26	BTB-00926636	17857480	Milk	17906861	17906865	210405
215	26	BTA-62125-no-rs	17899619	Milk	17906861	17906865	210405
216	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	63654
217	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	199076
218	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	204129
219	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	204130
220	26	BTA-62125-no-rs	17899619	Milk	17914357	17914361	210407
221	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	63655
222	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	197826
223	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	203026
224	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	203027
225	26	BTA-62125-no-rs	17899619	Milk	17915872	17915876	209354
226	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	195860
227	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	201008
228	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	201009
229	26	BTA-62125-no-rs	17899619	Milk	17922107	17922111	206462
230	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	33984
231	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195668
232	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195669
233	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	195670
234	26	BTA-62125-no-rs	17899619	Milk	17928246	17928250	200758
235	26	BTA-62125-no-rs	17899619	Milk	17931209	17931213	199092
236	26	BTA-62125-no-rs	17899619	Milk	17931209	17931213	204151
237	28	BTA-99379-no-rs	41666186	Reproduction	41646434	41646438	107048
238	28	BTA-99379-no-rs	41666186	Reproduction	41646434	41646438	107227
239	28	ARS-BFGL-NGS-11935	44942879	Reproduction	44971508	44971512	238444
240	28	ARS-BFGL-NGS-11935	44942879	Production	44981682	44981686	66703
241	28	ARS-BFGL-NGS-11935	44942879	Production	44981682	44981686	66704
242	28	ARS-BFGL-NGS-11935	44942879	Production	44981682	44981686	69429
243	28	Hapmap43005-BTA-64606	45805105	Milk	45811156	45811160	34467

18.1 QTL annotation on GALLO

QTL type

#GALLO
par(mar=c(8,20,8,8))
plot_qtl_info(out.qtl, qtl_plot = "qtl_type", cex=1)

QTL type

#GALLO
par(mar=c(10,20,10,10))
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Reproduction")

par(mar=c(10,20,10,10))
plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class="Health")

Reproduction

Health

18.2 QTL enrichment on GALLO

#QTL enrichment analysis 
out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]

write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/Bonf_5_15/out_enrich_qtl_genome_name_15.csv")

out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/Bonf_5_15/out_enrich_qtl_genome_type_15.csv")

#Plots

#Name

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL," - ","CHR",out.enrich_qtl_name$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL," - ","CHR",out.enrich_qtl_type$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval	QTL_type
15	Milk C14 index	35	4437	209	163224	0.0000000	0.0000000	Milk
28	Milk myristoleic acid content	26	3047	209	163224	0.0000000	0.0000000	Milk
31	Milk protein percentage	39	8803	209	163224	0.0000000	0.0000000	Milk
29	Milk palmitoleic acid content	15	2422	209	163224	0.0000007	0.0000079	Milk
16	Milk C16 index	12	2002	209	163224	0.0000129	0.0001159	Milk
36	Muscle carnosine content	3	67	209	163224	0.0000933	0.0006998	Meat and Carcass
23	Milk lactose content	3	105	209	163224	0.0003523	0.0022648	Milk
20	Milk fat content	2	92	209	163224	0.0063319	0.0356168	Milk
27	Milk myristic acid content	4	902	209	163224	0.0294859	0.1449683	Milk
33	Milk stearic acid content	2	218	209	163224	0.0322152	0.1449683	Milk
30	Milk pentadecylic acid content	2	280	209	163224	0.0505491	0.2067917	Milk
44	Teat length	2	300	209	163224	0.0570974	0.2141153	Exterior
14	Milk arachidic acid content	1	60	209	163224	0.0740084	0.2220251	Milk
25	Milk margaric acid content	1	59	209	163224	0.0728207	0.2220251	Milk
26	Milk mid-infrared spectra	1	55	209	163224	0.0680550	0.2220251	Milk
8	Ketosis	2	441	209	163224	0.1101333	0.2915293	Health
40	Rear leg placement - side view	2	430	209	163224	0.1056354	0.2915293	Exterior
39	Pregnancy rate	3	944	209	163224	0.1218012	0.3045029	Reproduction
37	Myristic acid content	1	120	209	163224	0.1425637	0.3376510	Meat and Carcass
6	Fat thickness at the 12th rib	1	169	209	163224	0.1947851	0.4382665	Meat and Carcass
7	Foot angle	2	672	209	163224	0.2129475	0.4563160	Exterior
13	Maturity rate	1	243	209	163224	0.2677107	0.5072331	Production
17	Milk C18 index	1	246	209	163224	0.2705243	0.5072331	Milk
42	Stillbirth	3	1363	209	163224	0.2544359	0.5072331	Reproduction
18	Milk capric acid content	2	912	209	163224	0.3259016	0.5640605	Milk
38	Net merit	2	903	209	163224	0.3216964	0.5640605	Production
24	Milk lauric acid content	1	366	209	163224	0.3746664	0.6021425	Milk
32	Milk protein yield	5	3093	209	163224	0.3633449	0.6021425	Milk
41	Somatic cell score	2	1122	209	163224	0.4213659	0.6538437	Health
5	Dairy form	1	514	209	163224	0.4829472	0.7244208	Exterior
3	Calving ease	5	3819	209	163224	0.5419946	0.7725995	Reproduction
9	Lean meat yield	1	621	209	163224	0.5494041	0.7725995	Meat and Carcass
43	Strength	1	664	209	163224	0.5736508	0.7813075	Exterior
45	Udder depth	1	695	209	163224	0.5903212	0.7813075	Exterior
19	Milk caprylic acid content	1	811	209	163224	0.6471450	0.8320436	Milk
10	Length of productive life	2	2004	209	163224	0.7280866	0.8855108	Production
35	Milking speed	1	1011	209	163224	0.7273020	0.8855108	Milk
2	Body weight gain	1	1354	209	163224	0.8248432	0.9440581	Production
4	Conception rate	1	1255	209	163224	0.8009513	0.9440581	Reproduction
11	Longissimus muscle area	1	1420	209	163224	0.8391628	0.9440581	Meat and Carcass
12	Marbling score	1	1817	209	163224	0.9037804	0.9830306	Meat and Carcass
34	Milk yield	5	6432	209	163224	0.9174952	0.9830306	Milk
1	Body weight	2	4289	209	163224	0.9746418	0.9967928	Production
21	Milk fat percentage	8	10941	209	163224	0.9726277	0.9967928	Milk
22	Milk fat yield	1	8220	209	163224	0.9999797	0.9999797	Milk

Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval
1	Exterior	9	9077	209	163224	0.8264587	1
2	Health	4	5889	209	163224	0.9456284	1
3	Meat and Carcass	8	18258	209	163224	0.9999640	1
4	Milk	168	75352	209	163224	0.0000000	0
5	Production	8	19640	209	163224	0.9999916	1
6	Reproduction	12	35008	209	163224	1.0000000	1

19 GPROFILER

online version: https://biit.cs.ut.ee/gprofiler/gost

I got a script from Julia Rodrigues about the R package GPROFILER to perform an enrichment of my Genes.

### enriquecimento genico
#install.packages("gprofiler2")
library(gprofiler2)

#Para conferir a lista de organism -> https://biit.cs.ut.ee/gprofiler/page/organism-list

#Obs: eu entro com os ids ENSOAR...
query <- read.table ("/home/bambrozi/2_CORTISOL/GALLO/Bonf_5_15/out_genes_50k_15.txt", header = T)
query <- query[,c("gene_id")]

gene_enrich <- gost(
  query,
  organism = "btaurus", 
  ordered_query = FALSE,
  multi_query = FALSE,
  significant = TRUE,
  exclude_iea = FALSE,
  measure_underrepresentation = FALSE,
  evcodes = TRUE,
  user_threshold = 0.05,
  correction_method = c("fdr"),
  domain_scope = c("annotated"),
  numeric_ns = "",
  sources = NULL,
  as_short_link = FALSE,
  highlight = FALSE
)


#str(gene_enrich) # para ver o formato dos meus dados

#selecionando apenas as informações da lista que me interessam para fazer meu data.frame 
result_enrich <- data.frame(gene_enrich$result)
result_enrich <- data.frame(Category = result_enrich$source,
                            ID = result_enrich$term_id,
                            Term = result_enrich$term_name,
                            adj_pvalue = result_enrich$p_value,
                            id_ensembl = result_enrich$intersection)

write.table(result_enrich,"/home/bambrozi/2_CORTISOL/GPROFILER/Bonf_5_15/gene_enrich_15.txt", col.names=TRUE, row.names=FALSE, sep="\t", quote=F)
write.csv(result_enrich,"/home/bambrozi/2_CORTISOL/GPROFILER/Bonf_5_15/gene_enrich_15.csv")

Below we can se the significant terms of this enrichment (output):

X	Category	ID	Term	adj_pvalue	id_ensembl
1	GO:MF	GO:0004352	glutamate dehydrogenase (NAD+) activity	0.0290548	ENSBTAG00000007540
2	GO:MF	GO:0004353	glutamate dehydrogenase [NAD(P)+] activity	0.0290548	ENSBTAG00000007540
3	GO:MF	GO:0016639	oxidoreductase activity, acting on the CH-NH2 group of donors, NAD or NADP as acceptor	0.0290548	ENSBTAG00000007540
4	GO:MF	GO:0031702	type 1 angiotensin receptor binding	0.0290548	ENSBTAG00000012393
5	GO:MF	GO:0031703	type 2 angiotensin receptor binding	0.0290548	ENSBTAG00000012393
6	GO:MF	GO:0005026	transforming growth factor beta receptor activity, type II	0.0414893	ENSBTAG00000019832
7	GO:MF	GO:0102193	protein-ribulosamine 3-kinase activity	0.0414893	ENSBTAG00000015414

19.1 GPROFILER ON-LINE

From the online version of GPROFILER i got the following results.

Legend

The GO CONTEXT provided by GPROFILER can be seen in the image below:

19.2 Terms distribution Pie Chart

There was no necessity to make this plot.

20 Haplotype Blocks

First I need to generate the .ped file

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean
RESULT=/home/bambrozi/2_CORTISOL/HAPLOBLOCK/inter_file

plink \
    --bfile ${DATA} \
    --cow \
    --recode \
    --out ${RESULT}

Second, is necessary to convert the .ped file to Linkage format:

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/HAPLOBLOCK/inter_file
RESULT=/home/bambrozi/2_CORTISOL/HAPLOBLOCK/hapblock_in

plink \
    --file ${DATA} \
    --cow \
    --recode HV \
    --out ${RESULT}

The code above will generate a pair of files .ped and .info for each chromosome

I ran the haploview only for chromosome 03, 11, 14, 19, 20, 22, 23, 26 and 28 that are when the significant SNP are located.

As the LD Plot bring the SNP ordered, first I looked at /home/bambrozi/2_CORTISOL/GWAS/SNP_sig_Bonf_ind_seg_15.csv, to know the SNP position, and then compare with the Check Markers tab in the haploview SNP position x SNP# in plot

VEP	rs	SNP	Chr	bp	A1	A2	Freq	Haploview.Order	Block
yes	rs109888380	ARS-BFGL-NGS-4585	3	107160292	A	C	0.3353170	1818
yes	rs41624254	BTA-122098-no-rs	11	31882807	A	C	0.4444440	674
no	rs42217767	BTB-01060598	11	32301594	C	A	0.4503970	680
yes	rs41632193	Hapmap39823-BTA-35254	14	64061208	A	C	0.2916670	1196
yes	rs41632223	UA-IFASA-7664	14	64299714	C	A	0.2678570	1200
yes	rs109751680	ARS-BFGL-NGS-28901	19	42338148	C	A	0.3710320	713
yes	rs41577586	ARS-BFGL-NGS-29119	19	49744592	A	C	0.4047620	849	Block 38
yes	rs42590312	BTB-01471673	20	11545685	A	C	0.2460320	232
yes	rs110031217	ARS-BFGL-NGS-23411	22	5134078	A	C	0.1865080	88
yes	rs110991998	ARS-BFGL-NGS-26419	22	46052082	A	C	0.0813492	762
no	rs29022819	Hapmap54016-rs29022819	23	33609998	C	A	0.1904760	534
yes	rs41644634	BTA-62125-no-rs	26	17899619	C	A	0.0257937	297	Block 10
yes	rs42089058	BTB-00926636	26	17857480	C	A	0.0257937	296	Block 10
yes	rs109578343	ARS-BFGL-NGS-11935	28	44942879	A	C	0.0297619	773
yes	rs41567074	BTA-99379-no-rs	28	41666186	A	C	0.0496032	701
yes	rs41648479	Hapmap43005-BTA-64606	28	45805105	A	C	0.0297619	791	Block 21

As can be observed in the column “Block”,four significant SNPs fell within a Haploblock.

rs41577586 (ARS-BFGL-NGS-29119) - chr 19 - order 849 - Block 38 rs41644634 (BTA-62125-no-rs) - chr 26 - order 297 - Block 10 rs42089058 (BTB-00926636) - chr 26 - order 296 - Block 10 rs41648479 (Hapmap43005-BTA-64606) - chr 28 - order 791 - Block 21

Chr 3 (SNP 1818)

Chr 11 (SNP 674 and 680)

Chr 14 (SNP 1196 and 1200)

Chr 19 (SNP 713)

Chr 19 (SNP 849)

Chr 20 (SNP 232)

Chr 22 (SNP 88)

Chr 22 (SNP 762)

Chr 23 (SNP 534)

Chr 26 (SNP 296 and 297)

Chr 28 (SNP 701)

Chr 28 (SNP 773 and 791)

21 Genetic Correlation

To assess the correlation between Cortisol phenotypes and Genomic Estimated Breeding Values (GEBVs), we opt for a linear regression instead of a standard correlation test. This decision is driven by the non-normal distribution of our Cortisol phenotypes, which violates the assumptions required for traditional correlation tests.

Linear regression offers a robust alternative as it does not necessitate normality for the dependent variable. By regressing GEBVs over Cortisol, we can model the relationship between these variables. Our aim is to estimate the regression coefficient, which serves as our correlation estimate.

Due to the violation of normality assumptions for the dependent variable (Cortisol), traditional correlation tests may not provide reliable results, particularly in assessing the significance of the correlation. Therefore, alternative approaches, such as linear regression, are preferred as they do not require the same assumptions about the distribution of the dependent variable. By using linear regression, we can still assess the relationship between Cortisol and GEBVs while accommodating the non-normality of Cortisol phenotypes.

The regression model can be represented as follows: \[ y = \beta_0 + \beta_1 \times GEBV_{\text{Milk}} + \epsilon \]

Where:

• \(y\) represents Cortisol phenotypes.
• \(GEBV_{\text{Milk}}\) denotes the GEBV for Milk Yield.
• \(\beta_0\) and \(\beta_1\) are the intercept and regression coefficient, respectively.
• \(\epsilon\) represents the error term capturing unexplained variability.

This approach enables us to quantify the relationship between Cortisol and GEBVs, addressing the non-normality of Cortisol phenotypes while allowing for formal hypothesis testing of the correlation’s significance.

21.1 Data preparation

The first data I received from Lucas had only 135 animals out of 260 with values the other 125 had only NA I shown this to Lucas Lucas wrote to Alisson Lucas sent me the missing animals I merged this two files

rm(list = ls())

# Load the necessary library
library(dplyr)
library(tidyverse)

cortisol_260 <- read.csv("/home/bambrozi/2_CORTISOL/Data/data_clean.csv")

#This is the first dataframe with information for 135 animals and 125 NA
GEBVs1 <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/ebvs_elora.csv")
#This is the second file with information for the 125 NA animals
GEBVs2 <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/elora_missing_females_2404_06_11_2024.csv")
#This are de columns we can use because we know the meaning of the acronyms
GEBVs_to_use <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/ebv_names_lucas_06102024_BAG.csv")


sum(is.na(GEBVs1$MILK))
GEBVs1<- GEBVs1[which(is.na(GEBVs1[,"DHI_BARN_NAME"]) == F),]

sum(!is.na(GEBVs2$MILK))
GEBVs2<- GEBVs2[which(is.na(GEBVs2[,"DHI_BARN_NAME"]) == F),]

print(GEBVs1$DHI_BARN_NAME)
print(GEBVs2$DHI_BARN_NAME)

# Making the two dataframes with the same columns
# Remove elora_id and international_id from GEBVs1
GEBVs1 <- GEBVs1 %>% select(-elora_id, -international_id)

# Remove ANIMAL_ID from GEBVs2
GEBVs2 <- GEBVs2 %>% select(-ANIMAL_ID)

# Check if the two dataframes have the same columns
have_same_columns <- all(names(GEBVs1) == names(GEBVs2))

if (have_same_columns) {
  print("The dataframes have the same columns.")
} else {
  print("The dataframes do not have the same columns.")
}


# Check if the column names are in the same order
same_order <- identical(names(GEBVs1), names(GEBVs2))

if (same_order) {
  print("The columns are in the same order.")
} else {
  print("The columns are not in the same order.")
}

GEBVs_combined <- rbind(GEBVs1, GEBVs2)

# Sort the columns
sorted_cortisol_260 <- sort(cortisol_260$ID)
sorted_GEBVs_combined <- sort(GEBVs_combined$DHI_BARN_NAME)

# Check if the sorted columns have the same values
identical(sorted_cortisol_260, sorted_GEBVs_combined)

# Create a duplicate of the column 'DHI_BARN_NAME' and name it 'elora_id'
GEBVs_combined$elora_id <- GEBVs_combined$DHI_BARN_NAME

# Assuming GEBVs_combined is your data frame
GEBVs_combined <- GEBVs_combined %>%
  select(elora_id, DHI_BARN_NAME, everything())

write.csv(GEBVs_combined, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/ebvs_elora_complete.csv")

# Merging the dataframe with Cortisol values, with the dataframe with GEBVs values
Merg_Cort_GEBVs <- merge(cortisol_260, GEBVs_combined, by.x = "ID", by.y = "elora_id")

write.csv(Merg_Cort_GEBVs, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/Merged_Cortisol_GEBVs.csv")

#Opening the file with the GEBVs columns to use
Columns_to_use <- readLines("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/traits_to_use.txt")

colnames(Merg_Cort_GEBVs)[405] <- "IDD"

data <- select(Merg_Cort_GEBVs, ID, T4Cortisol, BIRTH_YEAR, all_of(Columns_to_use))

# The data below has the the 55 GEBVs + Cortisol data + Birth Year
write.csv(data, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits.csv")

samp_date2 <- read.csv("/home/bambrozi/2_CORTISOL/Data/Elora animal_ids_kl_sampling_date.csv")

# Convert Sampling_date to Date using as.Date
samp_date$Sampling_date <- as.Date(samp_date$Sampling_date, format = "%m/%d/%Y")

table(samp_date$Sampling_date)

samp_date <- select(samp_date, Elora_id, Sampling_date)

# Check if data$ID and samp_dates$elora_id are identical in values and order
identical(data$ID, samp_date$Elora_id)

data_final <- merge(data, samp_date, by.x="ID", by.y="Elora_id")

data_final <- data_final %>%
  select(ID, T4Cortisol, BIRTH_YEAR, Sampling_date, everything())

# The data below has the the 55 GEBVs + Cortisol data + Birth Year + Sampling data
write.csv(data_final, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits2.csv")

ps. I double checked by hand the select and merge process against the original tables received and is everything ok.

21.2 Correlations - Linear Regression

21.2.1 Test with one trait

# Fit the linear regression model
model <- lm(T4Cortisol ~ MILK, data = data)

# Summarize the model to get the regression coefficients and statistical summary
model_summary <- summary(model)

# Extract the desired statistics
multiple_r_squared <- model_summary$r.squared
adjusted_r_squared <- model_summary$adj.r.squared
f_statistic <- model_summary$fstatistic[1]
p_value <- pf(model_summary$fstatistic[1], model_summary$fstatistic[2], model_summary$fstatistic[3], lower.tail = FALSE)

# Combine the statistics into a data frame
results <- data.frame(
  Multiple_R_Squared = multiple_r_squared,
  Adjusted_R_Squared = adjusted_r_squared,
  F_Statistic = f_statistic,
  P_Value = p_value
)

# Save the results to a CSV file
write.csv(results, file = "/home/bambrozi/2_CORTISOL/Correlation/Results/regression_summary_milk.csv", row.names = FALSE)

# Plot the linear regression between T4Cortisol and MILK
ggplot(data, aes(x = MILK, y = T4Cortisol)) +
  geom_point() +  # Add points
  geom_smooth(method = "lm", se = FALSE, color = "blue") +  # Add linear regression line
  labs(title = "Linear Regression of T4Cortisol on MILK",
       x = "MILK",
       y = "T4Cortisol") +
  theme_minimal()

Plot linear regression Cortisol vs. Milk GEBV

Summary statistics

Multiple_R_Squared	Adjusted_R_Squared	F_Statistic	P_Value
5.2e-05	-0.0038237	0.0134257	0.9078463

21.2.2 Loop for all traits

As we have 54 GEBVs to fit a linear regression we designed a loop:

# Initialize a list to store the results
results_list <- list()

# Loop through columns 4 to ncol(data) for the GEBVs
for (i in 4:ncol(data)) {
  trait_name <- colnames(data)[i]
  
  # Fit the linear regression model
  model <- lm(data[[2]] ~ data[[i]], data = data)
  
  # Summarize the model
  model_summary <- summary(model)
  
  # Extract the desired statistics
  multiple_r_squared <- model_summary$r.squared
  adjusted_r_squared <- model_summary$adj.r.squared
  f_statistic <- model_summary$fstatistic[1] # F-statistic value
  f_num_df <- model_summary$fstatistic[2] # Numerator degrees of freedom
  f_den_df <- model_summary$fstatistic[3] # Denominator degrees of freedom
  p_value <- pf(f_statistic, f_num_df, f_den_df, lower.tail = FALSE) # P-value
  
  # Extract the coefficient and its p-value for the trait
  coef_summary <- coef(model_summary)
  trait_coef <- coef_summary[2, "Estimate"]  # Assumes the trait is the second predictor
  trait_p_value <- coef_summary[2, "Pr(>|t|)"]
  
  # Combine the statistics into a data frame
  result <- data.frame(
    Trait = trait_name,
    Multiple_R_Squared = multiple_r_squared,
    Adjusted_R_Squared = adjusted_r_squared,
    F_Statistic = f_statistic,
    P_Value = p_value,
    Coefficient = trait_coef,
    Coefficient_P_Value = trait_p_value
  )
  
  # Append the result to the results list
  results_list[[i - 3]] <- result
}

# Combine all results into a single data frame
results_df_0 <- do.call(rbind, results_list)

# Save the results to a CSV file
write.csv(results_df, file = "/home/bambrozi/2_CORTISOL/Correlation/Results/regression_summary_all_traits.csv", row.names = FALSE)

Summary statistics for all Traits’ GEBVs

Trait	Multiple_R_Squared	Adjusted_R_Squared	F_Statistic	P_Value	Coefficient	Coefficient_P_Value
BMR	0.0400473	0.0363266	10.7632421	0.0011781	21.1128882	0.0011781
CO	0.0280949	0.0243278	7.4580136	0.0067510	-13.2769834	0.0067510
MSL	0.0198445	0.0160455	5.2235464	0.0230946	-16.9454262	0.0230946
CTFS	0.0185227	0.0147186	4.8690531	0.0282237	15.8469990	0.0282237
UT	0.0166425	0.0128310	4.3664218	0.0376334	-15.6741466	0.0376334
MS	0.0128559	0.0090298	3.3600230	0.0679494	-14.7558389	0.0679494
CK	0.0106156	0.0067808	2.7682148	0.0973674	14.6866464	0.0973674
IH	0.0101222	0.0062855	2.6382359	0.1055404	-7.3062481	0.1055404
DD	0.0100161	0.0061790	2.6103042	0.1073935	-9.5352332	0.1073935
CONF	0.0095452	0.0057062	2.4863924	0.1160600	-13.9681677	0.1160600
UD	0.0085295	0.0046866	2.2195470	0.1374945	-15.7518165	0.1374945
SU	0.0077590	0.0039131	2.0174678	0.1567056	7.3716271	0.1567056
MT	0.0076274	0.0037810	1.9829901	0.1602796	-10.6502458	0.1602796
LP	0.0074242	0.0035770	1.9297608	0.1659824	9.2112199	0.1659824
MET	0.0070642	0.0032156	1.8355367	0.1766601	-7.6934858	0.1766601
MR	0.0066755	0.0028254	1.7338637	0.1890867	-7.8752018	0.1890867
DO	0.0060791	0.0022267	1.5779921	0.2101864	9.5682702	0.2101864
DHL	0.0052895	0.0014340	1.3719419	0.2425591	-9.4618477	0.2425591
MOB	0.0047368	0.0008792	1.2279220	0.2688435	7.8139790	0.2688435
DF	0.0046402	0.0007822	1.2027541	0.2737945	7.8603320	0.2737945
FAT	0.0046204	0.0007623	1.1975980	0.2748229	1.0090961	0.2748229
SH	0.0043235	0.0004643	1.1203076	0.2908422	5.2472606	0.2908422
DS	0.0043121	0.0004528	1.1173348	0.2914818	-7.5550660	0.2914818
FOOT	0.0043005	0.0004412	1.1143288	0.2921304	26.7441735	0.2921304
FA	0.0042762	0.0004168	1.1079850	0.2935052	-8.0817790	0.2935052
PROT	0.0040026	0.0001421	1.0368190	0.3095163	1.2915168	0.3095163
WL	0.0038262	-0.0000350	0.9909414	0.3204451	5.2197508	0.3204451
MDR	0.0038096	-0.0000516	0.9866366	0.3214965	7.0545996	0.3214965
CA	0.0036379	-0.0002239	0.9420177	0.3326685	7.4603427	0.3326685
ME	0.0034176	-0.0004452	0.8847561	0.3477821	-5.0950658	0.3477821
SCS	0.0031318	-0.0007321	0.8105371	0.3688011	-4.8756313	0.3688011
FE	0.0028874	-0.0009774	0.7470985	0.3881993	-5.1774356	0.3881993
FTP	0.0027874	-0.0010778	0.7211475	0.3965550	9.8351267	0.3965550
AFS	0.0025212	-0.0013450	0.6521188	0.4201001	-5.9185799	0.4201001
UF	0.0018686	-0.0020001	0.4830008	0.4876916	6.8229142	0.4876916
ST	0.0018050	-0.0020640	0.4665276	0.4952019	-7.1228081	0.4952019
MSP	0.0014890	-0.0023812	0.3847251	0.5356327	-3.5290172	0.5356327
HL	0.0013205	-0.0025503	0.3411423	0.5596809	-4.3615962	0.5596809
SCK	0.0011411	-0.0027305	0.2947310	0.5876733	3.3934702	0.5876733
TL	0.0009008	-0.0029717	0.2326179	0.6299983	-5.0172076	0.6299983
LOC	0.0008542	-0.0030185	0.2205721	0.6390011	-3.5628830	0.6390011
DA	0.0006826	-0.0031907	0.1762328	0.6749803	3.5280623	0.6749803
CW	0.0005859	-0.0032878	0.1512479	0.6976664	-2.7721164	0.6976664
BCS	0.0003594	-0.0035151	0.0927672	0.7609338	2.1412729	0.7609338
RUM	0.0003149	-0.0035598	0.0812707	0.7758114	-1.6952340	0.7758114
TU	0.0002204	-0.0036547	0.0568793	0.8116875	1.0400512	0.8116875
FL	0.0001793	-0.0036960	0.0462618	0.8298705	1.5296709	0.8298705
BQ	0.0001697	-0.0037056	0.0437957	0.8343993	-1.5434870	0.8343993
HHE	0.0000521	-0.0038236	0.0134478	0.9077707	0.6475671	0.9077707
MILK	0.0000520	-0.0038237	0.0134257	0.9078463	0.0042447	0.9078463
HH	0.0000509	-0.0038249	0.0131340	0.9088482	-0.6606061	0.9088482
BD	0.0000293	-0.0038466	0.0075535	0.9308097	-0.6433874	0.9308097
DCA	0.0000046	-0.0038713	0.0011932	0.9724707	-0.2685932	0.9724707
FEED	0.0000001	-0.0038759	0.0000152	0.9968903	-0.0190613	0.9968903

Five out 54 traits GEBVs presented significant correlations with Cortisol:

• BMR = Body Maintenance Requirements
• CO = Cystic ovaries
• MSL = Median Suspensory Ligament
• CTFS = Calving to First Service
• UT = Udder Texture

21.2.3 Adding BIRTH_YEAR

21.2.3.1 Model Description

The regression model added the BY is shown bellow:

\[ y = \beta_0 + \beta_1 \times GEBV_{\text{Trait}} + BIRTH\_YEAR + \epsilon \]

Where:

• \(y\) represents Cortisol phenotypes.
• \(GEBV_{\text{Trait}}\) denotes the GEBV for the specific trait (e.g., Milk Yield).
• \(BIRTH\_YEAR\) is the birth year of the subjects, included as a factor.
• \(\beta_0\) and \(\beta_1\) are the intercept and regression coefficient, respectively.
• \(\epsilon\) represents the error term capturing unexplained variability.

The BIRTH_YEAR variable is converted to a factor to account for the categorical nature of birth years.

# Convert BIRTH_YEAR to a factor and rename
data$BIRTH_YEAR <- as.factor(data$BIRTH_YEAR)

# Initialize a list to store the results
results_list <- list()

# Loop through columns 3 to ncol(data) for the GEBVs
for (i in 4:ncol(data)) {
  trait_name <- colnames(data)[i]
  
  # Fit the linear regression model with BIRTH_YEAR as an additional predictor
  model <- lm(data[[2]] ~ data[[i]] + BIRTH_YEAR, data = data)
  
  # Summarize the model
  model_summary <- summary(model)
  
  # Extract the desired statistics
  multiple_r_squared <- model_summary$r.squared
  adjusted_r_squared <- model_summary$adj.r.squared
  f_statistic <- model_summary$fstatistic[1] # F-statistic value
  f_num_df <- model_summary$fstatistic[2] # Numerator degrees of freedom
  f_den_df <- model_summary$fstatistic[3] # Denominator degrees of freedom
  p_value <- pf(f_statistic, f_num_df, f_den_df, lower.tail = FALSE) # P-value
  
  # Extract the coefficient and its p-value for the trait
  coef_summary <- coef(model_summary)
  trait_coef <- coef_summary[2, "Estimate"]  # Assumes the trait is the second predictor
  trait_p_value <- coef_summary[2, "Pr(>|t|)"]
  
  # Combine the statistics into a data frame
  result <- data.frame(
    Trait = trait_name,
    Multiple_R_Squared = multiple_r_squared,
    Adjusted_R_Squared = adjusted_r_squared,
    F_Statistic = f_statistic,
    P_Value = p_value,
    Coefficient = trait_coef,
    Coefficient_P_Value = trait_p_value
  )
  
  # Append the result to the results list
  results_list[[i - 2]] <- result
}

# Combine all results into a single data frame
results_df <- do.call(rbind, results_list)

# Save the results to a CSV file
write.csv(results_df, file = "/home/bambrozi/2_CORTISOL/Correlation/Results/add_BY/regression_summary_all_traits_BY.csv", row.names = FALSE)

Summary statistics for all Traits’ GEBVs adding BIRTY_YEAR

Trait	Multiple_R_Squared	Adjusted_R_Squared	F_Statistic	P_Value	Coefficient	Coefficient_P_Value
BMR	0.0881915	0.0738886	6.165997	0.0000945	19.1527245	0.0028131
CO	0.0803421	0.0659161	5.569252	0.0002590	-12.5790675	0.0094097
MSL	0.0734487	0.0589145	5.053527	0.0006186	-16.2613977	0.0277614
IH	0.0719150	0.0573568	4.939831	0.0007494	-9.3449051	0.0354801
CTFS	0.0705440	0.0559642	4.838504	0.0008891	14.3231800	0.0442704
UT	0.0704128	0.0558310	4.828827	0.0009037	-14.8606501	0.0452229
MS	0.0686230	0.0540132	4.697043	0.0011284	-14.8462730	0.0606040
CK	0.0670600	0.0524256	4.582365	0.0013689	15.4450943	0.0785803
PROT	0.0658116	0.0511577	4.491053	0.0015963	2.1108214	0.0970292
DD	0.0638377	0.0491528	4.347166	0.0020332	-8.6326827	0.1365405
FAT	0.0628176	0.0481167	4.273045	0.0023028	1.2796392	0.1637385
SU	0.0623970	0.0476895	4.242529	0.0024239	7.0165690	0.1767005
CONF	0.0621537	0.0474424	4.224892	0.0024968	-11.6231634	0.1847326
MET	0.0618359	0.0471196	4.201861	0.0025952	-7.2376432	0.1958719
MR	0.0617517	0.0470341	4.195767	0.0026218	-7.5588944	0.1989504
FOOT	0.0614269	0.0467042	4.172251	0.0027273	31.1509094	0.2113774
MOB	0.0613973	0.0466741	4.170111	0.0027372	8.6217376	0.2125534
LP	0.0613481	0.0466241	4.166549	0.0027536	8.0864569	0.2145285
FTP	0.0608284	0.0460963	4.128971	0.0029327	13.5010333	0.2367661
DO	0.0607904	0.0460577	4.126220	0.0029463	8.8960747	0.2385010
ME	0.0607825	0.0460496	4.125648	0.0029491	-6.2944604	0.2388636
UD	0.0606216	0.0458862	4.114024	0.0030071	-12.1091142	0.2463830
FA	0.0605549	0.0458185	4.109209	0.0030315	-8.6648464	0.2495819
MT	0.0601784	0.0454361	4.082026	0.0031728	-8.2421107	0.2686411
CA	0.0599002	0.0451535	4.061948	0.0032814	8.3011337	0.2838987
DF	0.0598369	0.0450892	4.057381	0.0033066	7.4854663	0.2875213
DHL	0.0595211	0.0447684	4.034611	0.0034352	-8.1319072	0.3064961
SH	0.0586677	0.0439017	3.973163	0.0038074	4.4345159	0.3666760
MILK	0.0585384	0.0437704	3.963861	0.0038671	0.0328321	0.3771595
MDR	0.0584166	0.0436466	3.955101	0.0039243	6.0467308	0.3874240
ST	0.0578881	0.0431099	3.917120	0.0041817	-7.9781463	0.4369789
SCS	0.0576964	0.0429152	3.903356	0.0042790	-3.9614430	0.4573264
AFS	0.0575918	0.0428089	3.895845	0.0043331	-5.2064660	0.4690656
MSP	0.0575087	0.0427245	3.889880	0.0043765	-3.9533312	0.4787399
DS	0.0573283	0.0425413	3.876936	0.0044723	-4.7841746	0.5009026
WL	0.0572605	0.0424724	3.872073	0.0045088	3.4479700	0.5096796
TL	0.0572547	0.0424665	3.871655	0.0045119	-6.7563254	0.5104479
FE	0.0572054	0.0424165	3.868123	0.0045386	-3.8209975	0.5170084
DA	0.0566761	0.0418789	3.830184	0.0048356	4.3714958	0.5986609
HHE	0.0565353	0.0417358	3.820095	0.0049178	2.6941677	0.6249126
HL	0.0563327	0.0415301	3.805592	0.0050384	-3.1669335	0.6676242
LOC	0.0562600	0.0414563	3.800388	0.0050823	-3.0195770	0.6847865
FL	0.0562527	0.0414489	3.799865	0.0050868	2.8115963	0.6865743
SCK	0.0560192	0.0412117	3.783153	0.0052307	1.9379506	0.7520160
CW	0.0559471	0.0411384	3.777995	0.0052759	-1.9950607	0.7767448
FEED	0.0559443	0.0411356	3.777797	0.0052777	-1.3559978	0.7777588
BQ	0.0559195	0.0411104	3.776021	0.0052933	-1.9570851	0.7870649
TU	0.0558834	0.0410737	3.773438	0.0053162	1.0731908	0.8014578
UF	0.0558449	0.0410347	3.770689	0.0053406	2.2354069	0.8181403
DCA	0.0557115	0.0408991	3.761148	0.0054263	-1.0069789	0.8965483
BD	0.0557011	0.0408885	3.760401	0.0054331	0.8702554	0.9055126
BCS	0.0556786	0.0408657	3.758795	0.0054477	0.6193741	0.9285710
RUM	0.0556595	0.0408464	3.757433	0.0054601	-0.3154634	0.9570449
HH	0.0556561	0.0408428	3.757185	0.0054623	-0.2539972	0.9646162

Fitting Birth_Year to the model made one more trait significativally correlated with cortisol and enhanced the proportio of variation explained by the model:

• BMR = Body Maintenance Requirements
• CO = Cystic ovaries
• MSL = Median Suspensory Ligament
• CTFS = Calving to First Service
• UT = Udder Texture
• IH = Interdigital Hyperplasia

Below we can check the improvement in the percentage of variation in Cortisol explained by the Trait_GEBVs + Birth_Year.

CORR_BY <- read.csv("/home/bambrozi/2_CORTISOL/Correlation/Results/add_BY/regression_summary_all_traits_BY.csv")

CORR_MIN <- read.csv("/home/bambrozi/2_CORTISOL/Correlation/Results/regression_summary_all_traits.csv")

CORR_BY <- CORR_BY[, c("Trait", "Adjusted_R_Squared"), drop = FALSE]

CORR_MIN <- CORR_MIN[, c("Trait", "Adjusted_R_Squared"), drop = FALSE]

colnames(CORR_BY)[colnames(CORR_BY) == "Adjusted_R_Squared"] <- "Adjusted_R_Squared_BY"

colnames(CORR_MIN)[colnames(CORR_MIN) == "Adjusted_R_Squared"] <- "Adjusted_R_Squared_MIN"

Corr_R2_comp <- merge(CORR_MIN, CORR_BY, by.x = "Trait", by.y = "Trait")

Corr_R2_comp$Absolute_Change <- Corr_R2_comp$Adjusted_R_Squared_BY - Corr_R2_comp$Adjusted_R_Squared_MIN

write.csv(Corr_R2_comp, "/home/bambrozi/2_CORTISOL/Correlation/Results/add_BY/absolute_improv_r2.csv")

X	Trait	Adjusted_R_Squared_Model_MIN	Adjusted_R_Squared_MIN.BY	Absolute_Change
1	AFS	-0.0013450	0.0428089	0.0441539
2	BCS	-0.0035151	0.0408657	0.0443809
3	BD	-0.0038466	0.0408885	0.0447351
4	BMR	0.0363266	0.0738886	0.0375621
5	BQ	-0.0037056	0.0411104	0.0448160
6	CA	-0.0002239	0.0451535	0.0453774
7	CK	0.0067808	0.0524256	0.0456448
8	CO	0.0243278	0.0659161	0.0415882
9	CONF	0.0057062	0.0474424	0.0417362
10	CTFS	0.0147186	0.0559642	0.0412457
11	CW	-0.0032878	0.0411384	0.0444262
12	DA	-0.0031907	0.0418789	0.0450696
13	DCA	-0.0038713	0.0408991	0.0447705
14	DD	0.0061790	0.0491528	0.0429738
15	DF	0.0007822	0.0450892	0.0443070
16	DHL	0.0014340	0.0447684	0.0433344
17	DO	0.0022267	0.0460577	0.0438310
18	DS	0.0004528	0.0425413	0.0420884
19	FA	0.0004168	0.0458185	0.0454018
20	FAT	0.0007623	0.0481167	0.0473544
21	FE	-0.0009774	0.0424165	0.0433939
22	FEED	-0.0038759	0.0411356	0.0450115
23	FL	-0.0036960	0.0414489	0.0451449
24	FOOT	0.0004412	0.0467042	0.0462629
25	FTP	-0.0010778	0.0460963	0.0471741
26	HH	-0.0038249	0.0408428	0.0446677
27	HHE	-0.0038236	0.0417358	0.0455595
28	HL	-0.0025503	0.0415301	0.0440805
29	IH	0.0062855	0.0573568	0.0510713
30	LOC	-0.0030185	0.0414563	0.0444747
31	LP	0.0035770	0.0466241	0.0430471
32	MDR	-0.0000516	0.0436466	0.0436982
33	ME	-0.0004452	0.0460496	0.0464948
34	MET	0.0032156	0.0471196	0.0439039
35	MILK	-0.0038237	0.0437704	0.0475941
36	MOB	0.0008792	0.0466741	0.0457949
37	MR	0.0028254	0.0470341	0.0442086
38	MS	0.0090298	0.0540132	0.0449834
39	MSL	0.0160455	0.0589145	0.0428690
40	MSP	-0.0023812	0.0427245	0.0451057
41	MT	0.0037810	0.0454361	0.0416552
42	PROT	0.0001421	0.0511577	0.0510155
43	RUM	-0.0035598	0.0408464	0.0444062
44	SCK	-0.0027305	0.0412117	0.0439421
45	SCS	-0.0007321	0.0429152	0.0436472
46	SH	0.0004643	0.0439017	0.0434374
47	ST	-0.0020640	0.0431099	0.0451738
48	SU	0.0039131	0.0476895	0.0437764
49	TL	-0.0029717	0.0424665	0.0454382
50	TU	-0.0036547	0.0410737	0.0447284
51	UD	0.0046866	0.0458862	0.0411996
52	UF	-0.0020001	0.0410347	0.0430348
53	UT	0.0128310	0.0558310	0.0430000
54	WL	-0.0000350	0.0424724	0.0425074

Adjusted_R_Squared_Model_MIN = Model without Birth Year
Adjusted_R_Squared_MIN.BY = Model adding Birth Year

21.2.4 Adding BIRTH_YEAR and SAMPLING DATE

The regression model added the BY and SAMPLING DATE is shown bellow:

\[ y = \beta_0 + \beta_1 \times GEBV_{\text{Trait}} + BIRTH\_YEAR + SAMPLING\_DATE + \epsilon \]

Where:

• \(y\) represents Cortisol phenotypes.
• \(GEBV_{\text{Trait}}\) denotes the GEBV for the specific trait (e.g., Milk Yield).
• \(BIRTH\_YEAR\) is the birth year of the subjects, included as a factor.
• \(SAMPLING\_DATE\) is the cortisol sampling date for the subjects, included as a factor.
• \(\beta_0\) and \(\beta_1\) are the intercept and regression coefficient, respectively.
• \(\epsilon\) represents the error term capturing unexplained variability.

The SAMPLING_DATE variable is also converted to a factor to account for the categorical nature of sampling date.

# Convert BIRTH_YEAR to a factor and rename
data_final$BIRTH_YEAR <- as.factor(data_final$BIRTH_YEAR)

# Convert Sampling_data to a factor and rename
data_final$Sampling_date <- as.factor(data_final$Sampling_date)

# Initialize a list to store the results
results_list <- list()

# Loop through columns 3 to ncol(data) for the GEBVs
for (i in 5:ncol(data_final)) {
  trait_name <- colnames(data_final)[i]
  
  # Fit the linear regression model with BIRTH_YEAR as an additional predictor
  model <- lm(data_final[[2]] ~ data_final[[i]] + data_final$BIRTH_YEAR + data_final$Sampling_date , data = data_final)
  
  # Summarize the model
  model_summary <- summary(model)
  
  # Extract the desired statistics
  multiple_r_squared <- model_summary$r.squared
  adjusted_r_squared <- model_summary$adj.r.squared
  f_statistic <- model_summary$fstatistic[1] # F-statistic value
  f_num_df <- model_summary$fstatistic[2] # Numerator degrees of freedom
  f_den_df <- model_summary$fstatistic[3] # Denominator degrees of freedom
  p_value <- pf(f_statistic, f_num_df, f_den_df, lower.tail = FALSE) # P-value
  
  # Extract the coefficient and its p-value for the trait
  coef_summary <- coef(model_summary)
  trait_coef <- coef_summary[2, "Estimate"]  # Assumes the trait is the second predictor
  trait_p_value <- coef_summary[2, "Pr(>|t|)"]
  
  # Combine the statistics into a data frame
  result <- data.frame(
    Trait = trait_name,
    Multiple_R_Squared = multiple_r_squared,
    Adjusted_R_Squared = adjusted_r_squared,
    F_Statistic = f_statistic,
    P_Value = p_value,
    Coefficient = trait_coef,
    Coefficient_P_Value = trait_p_value
  )
  
  # Append the result to the results list
  results_list[[i - 2]] <- result
}

# Combine all results into a single data frame
results_df <- do.call(rbind, results_list)

# Save the results to a CSV file
write.csv(results_df, file = "/home/bambrozi/2_CORTISOL/Correlation/Results/add_BY/regression_summary_all_traits_BY.csv", row.names = FALSE)

Summary statistics for all Traits’ GEBVs adding BIRTY_YEAR and SAMPLING_DATE

Trait	Multiple_R_Squared	Adjusted_R_Squared	F_Statistic	P_Value	Coefficient	Coefficient_P_Value
CO	0.3913617	0.3257757	5.967151	0	-11.1849879	0.0099861
BMR	0.3899307	0.3241904	5.931386	0	14.4612444	0.0135665
LP	0.3858330	0.3196512	5.829896	0	12.2619950	0.0330355
MILK	0.3850052	0.3187343	5.809559	0	0.0671160	0.0396652
PROT	0.3841834	0.3178238	5.789422	0	2.2166395	0.0476301
UT	0.3822859	0.3157218	5.743130	0	-12.1238483	0.0731558
CK	0.3801906	0.3134008	5.692345	0	12.7333709	0.1192726
HHE	0.3795579	0.3126999	5.677076	0	7.4003505	0.1388525
MSP	0.3793006	0.3124149	5.670876	0	-7.2388952	0.1478152
DA	0.3791391	0.3122360	5.666989	0	10.8037510	0.1537699
TU	0.3785722	0.3116080	5.653351	0	-5.2064639	0.1769405
MSL	0.3777098	0.3106526	5.632656	0	-8.6230634	0.2203509
BQ	0.3775224	0.3104450	5.628166	0	-7.7284891	0.2313766
IH	0.3772456	0.3101385	5.621542	0	-4.7354916	0.2488963
ST	0.3767980	0.3096426	5.610839	0	-9.9721644	0.2808121
LOC	0.3766890	0.3095218	5.608233	0	-7.2367609	0.2893509
FAT	0.3765584	0.3093772	5.605116	0	0.8520581	0.3000062
UD	0.3763515	0.3091480	5.600177	0	-9.3297651	0.3179533
CA	0.3757417	0.3084725	5.585641	0	6.0756898	0.3798799
MS	0.3755038	0.3082090	5.579979	0	-6.0375749	0.4085912
SCK	0.3754363	0.3081342	5.578372	0	-4.4161473	0.4173165
SCS	0.3754033	0.3080976	5.577587	0	3.9080681	0.4216769
IDD	0.3752677	0.3079474	5.574362	0	3.7029601	0.4403519
FOOT	0.3751830	0.3078536	5.572349	0	17.3309360	0.4526548
TL	0.3750803	0.3077398	5.569908	0	-6.6585204	0.4683140
MET	0.3749055	0.3075462	5.565755	0	-3.4014191	0.4970656
CTFS	0.3747600	0.3073850	5.562300	0	4.0557716	0.5233387
CONF	0.3746137	0.3072229	5.558828	0	-4.7896643	0.5523352
FE	0.3745078	0.3071056	5.556316	0	-2.9375586	0.5752627
HL	0.3743988	0.3069849	5.553731	0	3.4274416	0.6009102
FA	0.3742870	0.3068610	5.551080	0	-3.2821641	0.6298652
DD	0.3742836	0.3068573	5.551001	0	2.5402141	0.6307730
MOB	0.3742556	0.3068263	5.550337	0	3.0592146	0.6385442
FTP	0.3742359	0.3068045	5.549870	0	4.8583743	0.6441419
BCS	0.3741681	0.3067293	5.548263	0	2.6692352	0.6643615
HH	0.3740753	0.3066266	5.546066	0	2.0230249	0.6947785
DF	0.3740392	0.3065865	5.545209	0	2.3681806	0.7076996
FL	0.3739824	0.3065237	5.543865	0	-2.1711035	0.7294613
UF	0.3739605	0.3064994	5.543347	0	2.8880683	0.7384413
MDR	0.3738855	0.3064163	5.541571	0	1.8495373	0.7722558
WL	0.3738548	0.3063822	5.540843	0	1.2643028	0.7878801
AFS	0.3738293	0.3063540	5.540239	0	1.5814380	0.8018670
RUM	0.3738023	0.3063241	5.539601	0	-1.2574561	0.8179113
SH	0.3738015	0.3063233	5.539583	0	1.0342843	0.8184040
BD	0.3736958	0.3062061	5.537080	0	0.7613199	0.9071017
MR	0.3736957	0.3062060	5.537078	0	0.6291752	0.9071898
SU	0.3736871	0.3061965	5.536876	0	0.4745538	0.9186634
FEED	0.3736818	0.3061907	5.536751	0	0.3944746	0.9266753
DHL	0.3736774	0.3061858	5.536647	0	-0.5919776	0.9340753
DO	0.3736757	0.3061838	5.536604	0	0.5220723	0.9373261
DS	0.3736695	0.3061769	5.536458	0	0.3979132	0.9502668
CW	0.3736676	0.3061749	5.536414	0	0.3562368	0.9547808
ME	0.3736658	0.3061729	5.536370	0	-0.2401976	0.9599012
MT	0.3736595	0.3061659	5.536223	0	-0.0976386	0.9882642
DCA	0.3736593	0.3061657	5.536217	0	0.0802555	0.9906432

Fitting Birth_Year and Sampling_date to the model these are the traits with significant correlation:

• CO = Cystic ovaries
• BMR = Body Maintenance Requirements
• LP = Lactation persistency
• MILK = Milk yield
• PROT = Protein yield
• UT = Udder Texture